Enhanced high-dynamic-range imaging and tone mapping

ABSTRACT

The various embodiments of the present disclosure are directed towards methods for tone mapping High-Dynamic-Range (HDR) image data, as well as controlling the brightness of the image encoded by HDR the image data and/or the tone-mapped image data. HDR image is captured. A tone mapping function for the HDR image data is generated. To generate the tone mapping function, control points are dynamically determined based on an analysis of the HDR image data. The tone mapping function is fit to the control points. The tone mapping function is a non-linear function, and is described by a curve in a plane. The shape of the curve is constrained by a line generated from a portion of the control points. The tone mapping function is applied to the HDR image data. A color-compression is applied to the tone mapped image data to generate Standard Dynamic Range or Low Dynamic Range image data.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/526,902, filed Jul. 30, 2019, which is incorporated herein byreference in its entirety.

BACKGROUND

High-Dynamic-Range Imaging (HDRI) includes techniques that generateimage data of High Dynamic Range (HDR). That is, HDRI provides anincreased ratio of the largest possible pixel value (e.g., the largestpossible luminosity value) to the smallest possible pixel value (e.g.,the smallest possible luminosity value). Pixels of smaller luminosityvalues render as darker (e.g., blacker) regions of an encoded image,while pixels of larger luminosity values render as brighter (e.g.,whiter) regions of the image. Because of the enhanced ratio of thelargest to the smallest luminosity values, and when displayed on deviceswith sufficient capability to render the increased Dynamic Range (DR),HDR images may provide more detail and contrast, and thus may appearmore realistic and natural to the human viewer. For instance, whenrendered properly, an image of a scene that is encoded in HDR image datamay appear closer to what a human would actually observe when directlyviewing the scene or may otherwise be more visually appealing. However,many devices such as conventional displays and printers are not enabledto render HDR image data. Therefore, the HDR image data may be tonemapped to convert the HDR image data to lower dynamic range image data.

Conventional methods for tone mapping HDR image data may assume that thecamera's Auto Exposure (AE) settings are properly configured whencapturing the HDR image data. This assumption may fail in somesituations, resulting in a degraded tone-mapped image. Further, whencontrolling the brightness of an HDR image and/or the image encoded bytone-mapped HDR image data, some conventional methods may apply adigital gain function to the HDR image data. The digital gain functionmay degenerate many of the visual benefits associated with HDRI such asby rendering the HDR image and/or tone-mapped HDR image as appearing tobe “washed-out,” less realistic, or otherwise less visually appealing.

SUMMARY

Embodiments of the present disclosure are directed towards tone mappingof High Dynamic Range (HDR) image data, as well as controlling thebrightness of the tone mapped HDR image data. The tone mapped HDR imagedata may be transformed into Standard Dynamic Range (SDR) image data orLow Dynamic Range (LDR) via a compression of the pixel values, e.g., anapplication of a gamma-compression function on the tone-mapped HDR imagedata.

Rather than relying on the proper configuration of AE settings and adigital gain function, various embodiments may control image brightnessof HDR image data by generating a tone mapping function for the HDRimage data. When applied to the HDR image data, the tone mappingfunction maps the tone (e.g., the brightness) of the HDR image data suchthat the tone-transformed HDR image data may encode image brightnessthat matches the lighting conditions of the imaged scene, withoutrelying on a proper configuration of AE settings. The tone mappingfunction may also minimize visual artifacts due to the HDR imaging(e.g., flare-suppression and compression of highlights). Upon being tonemapped, the HDR image data may be compressed into SDR or LDR image datavia a filtering of the Least-Significant-Bits (LSBs) of the HDR pixelvalues.

One non-limiting embodiment includes capturing and/or receiving sourceimage data. The source image data may be HDR image data and mayrepresent and/or encode a source image. Tone control points aredetermined based on source pixel values of the source image data. Thedetermined tone control points may include a low-tone point, a mid-tonepoint, and a high-tone point. In some embodiments, the tone controlpoints may additionally include a flare-suppression point. A tonemapping function may be determined based on at least a portion of thetone control points. For example, the tone mapping function may be aparametric function that defines a curve (e.g., a Global Tone Curve),where the parameters of the function are fit such that the curve isconstrained to pass through (or include) the low-tone point, themid-tone point, and the high-tone point. In some embodiments, the curveis further constrained to pass through at least a portion of theadditionally determined points.

In at least one embodiment, determining the tone mapping function may befurther based on a gain value. The gain value may be determined based onthe mid-tone point and at least one other of the tone control points,such as but not limited to the flare-suppression point. The gain valuemay be determined to be equivalent to the slope of a gain line thatpasses through the mid-tone point and the flare-suppression point. Thefitting of the tone mapping function may be further constrained suchthat the derivative and/or instantaneous rate of change of the function,evaluated at one of the components of the mid-tone point, is at leastapproximately equivalent to the gain value. Target image data (e.g.,lower dynamic range image data) may be generated by transforming thesource image data encoding the source image, and/or source image dataencoding a subsequent frame, via an application of the tone mappingfunction. The target image data may include target pixel values, whichare defined by the application of the tone mapping function on the pixelvalues of the source image data.

BRIEF DESCRIPTION OF THE DRAWINGS

The present systems and methods for tone mapping high dynamic rangeimage data and controlling the brightness of the image is described indetail below with reference to the attached drawing figures, wherein:

FIG. 1 provides a schematic diagram of a high-dynamic-range imagingsystem, in accordance with some embodiments of the present disclosure;

FIG. 2A shows low-tone, mid-tone, high-tone, and flare-suppressioncontrol points embedded in 2D space spanned by a first basis vector fora first dimension corresponding to pixel values of source image data anda second basis vector for a second dimension corresponding to pixelvalues of target image data;

FIG. 2B shows a non-limiting embodiment of a plot of a tone mappingfunction, which is in accordance with the various embodiments;

FIG. 3 is a flow diagram showing a method for tone mapping high dynamicrange image data, in accordance with some embodiments of the presentdisclosure;

FIG. 4 is a flow diagram showing a method for generating lower dynamicrange image data from higher dynamic range image data, in accordancewith some embodiments of the present disclosure;

FIG. 5 is a flow diagram showing a method for distributing theoperations of tone mapping, in accordance with some embodiments of thepresent disclosure;

FIG. 6A is an illustration of an example autonomous vehicle, inaccordance with some embodiments of the present disclosure;

FIG. 6B is an example of camera locations and fields of view for theexample autonomous vehicle of FIG. 6A, in accordance with someembodiments of the present disclosure;

FIG. 6C is a block diagram of an example system architecture for theexample autonomous vehicle of FIG. 6A, in accordance with someembodiments of the present disclosure

FIG. 6D is a system diagram for communication between cloud-basedserver(s) and the example autonomous vehicle of FIG. 6A, in accordancewith some embodiments of the present disclosure; and

FIG. 7 is a block diagram of an example computing environment suitablefor use in implementing some embodiments of the present disclosure.

DETAILED DESCRIPTION

Systems and methods are disclosed that are related to High-Dynamic-RangeImaging (HDRI). More specifically, the embodiments herein relate to tonemapping High-Dynamic-Range (HDR) image data, as well as controlling thebrightness of the image encoded by the HDR image data and/or the imageencoded by the tone-mapped image data.

The tone mapped HDR image data may be transformed into Standard DynamicRange (SDR) image data or Low Dynamic Range (LDR) via a compression ofthe pixel values, e.g., an application of a gamma-compression functionon the tone-mapped HDR image data. One non-limiting embodiment includescapturing and/or receiving source image data. The source image data maybe HDR image data and may represent and/or encode a source image. Tonecontrol points are determined based on source pixel values of the sourceimage data. The determined tone control points may include a low-tonepoint, a mid-tone point, and/or a high-tone point. In some embodiments,the tone control points may additionally include a flare-suppressionpoint. In at least one embodiment, additional tone control points aredetermined.

A tone mapping function may be determined based on at least a portion ofthe control points. For example, the tone mapping function may be aparametric function that defines a curve (e.g., a Global Tone Curve).The parameters of the function may be fit such that the curve isconstrained to pass through (or include) the low-tone point, themid-tone point, and the high-tone point. In some embodiments, the curveis further constrained to pass through at least a portion of theadditionally determined points.

In at least one embodiment, determining the tone mapping function may befurther based on a gain value. The gain value may be determined based onthe mid-tone point and at least one other of the tone control points,such as but not limited to the flare-suppression point. The gain valuemay be determined to be equivalent to the slope of a gain line thatpasses through the mid-tone point and the flare-suppression point. Thefitting of the tone mapping function may be further constrained suchthat the derivative and/or instantaneous rate of change of the function,evaluated at one of the components of the mid-tone point, is at leastapproximately equivalent to the gain value. Target image data may begenerated by transforming the source image data, via an application ofthe tone mapping function on the source image data. The target imagedata may include target pixel values, which are defined by theapplication of the tone mapping function on the pixel values of thesource image data.

In contrast to conventional approaches, the various embodiments enabletone mapping HDR image data without relying on AE settings. As explainedbelow, the images that are tone-mapped may have more detail andcontrast, as compared to images that are tone-mapped via conventionalmethods. Furthermore, the various embodiments enable controlling theoverall image brightness of the HDR image and/or the tone-mapped imagewithout the application of the digital gain function. Thus, imagesgenerated by the various embodiments may not appear “washed-out” insituations where conventionally generated images would. Additionally,embodiments may successfully suppress flares (e.g., a positive blackpoint in the image data or errors in the black level subtraction), aswell as compress highlights (e.g., pixels with significant luminosityvalues) in the HDR image data. The various embodiments additionallyprovide enhanced methods for compressing HDR image data into StandardDynamic Range (SDR) or Low Dynamic Range (LDR) image data, whileconserving much of the critical information that encodes the increaseddetail and contrast of the HDR image data.

Some HDRI techniques of the various embodiments include combining orblending multiple captured SDR images of the same scene, with separateexposure settings or periods for each of the multiple SDR images. An HDRimage (encoded by HDR image data) may be generated by pixel values ofthe multiple SDR images. For example, a determination of the HDR image'spixel values for the darker regions of an imaged scene may be dominatedby the corresponding pixel values of the SDR images with longer exposuretimes. Using the pixel values generated by longer exposure times mayenable capturing greater detail and contrast in the darker regions ofthe scene. The determination of the HDR pixel values for the lighterregions of the scene may be dominated by the corresponding pixel valuesof the SDR images captured with shorter exposure times. Using the pixelvalues generated by shorter exposure times may prevent a “washout” orover-exposed effect on the lighter or brighter regions of the scene. Inother embodiments, HDR image data may be generated from a single image,where the images sensors (e.g., camera pixels) corresponding to thedarker regions are captured for longer exposure periods, and the imagesensors corresponding to the lighter regions are captured for shorterexposure periods.

As noted above, conventional HDR cameras and systems rely on a user toappropriately configure the AE settings of their camera. Such AEsettings may include Auto Exposure Bracketing (AEB) settings and/orvarious AE modes (e.g., night and day AE modes). These AE settings arerelatively static, and a user may rarely change the settings to matchtheir current environment. Furthermore, only a few preconfigured modesmay be available to the user (e.g., day mode or night mode)—none ofwhich may be consistent with the current environment. For example, aconventional HDR camera may not provide separate AE modes for a sunnyday, an overcast day, or states therebetween.

When AE modes do not adequately provide exposure settings that areconsistent with the scene's current lighting conditions, the overallbrightness of the HDR image may not realistically reflect the scene'slighting conditions. For instance, the HDR image may not render thescene as brightly lit as the scene's current lighting conditionsprovide. To compensate for this lighting mismatch, conventional HDRcameras and systems often employ a digital gain function to adjust orboost the luminosity of the HDR pixel values. Under various lightingconditions and/or AE settings, the gain value applied to the pixels maybe significant. Such large gains often saturate and/or clip the brighterregions of the HDR image, which may leave these regions to appearwashed-out or overexposed when rendered. Thus, in these variousscenarios, conventional HDR cameras and systems fail to achieve theincreased detail and contrast that the user was intending to capture viaHDRI.

Conventional HDR cameras and systems are associated with additionallimitations. For example, when an SDR device renders conventional HDRimage data, much of the increased detail and contrast of theconventional HDR image data may be lost. To encode an increased DR, HDRimage data may include pixels of greater depth than those of SDR images.For instance, the pixel depth of SDR image data may be 24 bits (8 bitsper color channel), while the pixel depth of some HDR image data may beas great or greater than 96 bits (32 bits per color channel). Becausemany display devices, data streams, storage devices, and printers (e.g.,SDR devices) cannot display, stream, store, and/or print pixels of suchdepth, HDR image data often is transformed to shallower pixel depth. Todo so, tone mapping may be used to transform (or map) HDR pixel valuesto SDR pixel values that may be more suitable for SDR devices. Forexample, tone mapping may be employed to transform HDR image data toSDR, or even Low Dynamic Range (LDR), image data. As discussed below,conventional tone mapping may result in a significant loss of the detailand contrast of the HDR image.

Conventional tone mapping may result in a lossy compression of the HDRimage data, and in many scenarios, significantly degrade the quality ofthe lower dynamic range image or a standard dynamic range image, ascompared to the HDR image. More specifically, conventional tone mappingmay be limited in its ability to conserve the critical information ofHDR image data that enables HDR images to appear natural and realisticto the human viewer. Thus, via conventional tone mapping, much of thecritical HDR information that renders the increased detail and contrastmay be lost in the compression procedure.

Conventional tone mapping may not conserve a substantial amount of thecritical information of the HDR image data especially when the abovediscussed AE settings and/or modes are inappropriate for the scene'scurrent lighting conditions. For instance, when imaging a dimlyilluminated scene, the user may fail to transition a conventional HDRcamera from day mode to night mode. The HDR image may appearunder-exposed because the HDR image data fails to encode much of thedetail and contrast of the darker regions of the imaged scene. As aresult, when generating an SDR (or an LDR) image from the under-exposedHDR image data the under-exposed appearance of the SDR image may be evenmore apparent.

Even when the AE settings are appropriate for the current lightingconditions, the lighting conditions may be dynamic across short temporalspans, while the AE settings are held constant over these short timespans. Because conventional tone mapping is applied at the frame-level,conventional mapping may not readily account for dynamic lightingconditions. For example, during capture of HDR video image data, arelatively bright object (e.g., a highly-reflective object or an objectthat includes a light source) may enter the scene and the current AEsettings may be inappropriate for the introduction of the bright object.Conventional tone mapping may render the dynamic brightly lit object asoverexposed, and the overall brightness of the video image data mayfluctuate.

Rather than relying on the proper configuration of AE settings anddigital gain, various embodiments may control the brightness bygenerating a tone mapping function for the HDR image data. In somenon-limiting embodiments, the tone mapping function may be a Global ToneMapping (GTM) function and/or a Global Tone Curve (GTC). The tonemapping function may be dynamically and/or globally determined based onthe HDR image. As such, the tone mapping function may be employed todynamically and globally tone map the HDR image data. When applied tothe HDR image data, the tone mapping function maps the tone (e.g., thebrightness) of the HDR image data such that the tone-transformed HDRimage data may encode image brightness that matches the lightingconditions of the imaged scene, without relying on a properconfiguration of AE settings. The tone mapping function may alsominimize visual artifacts due to the HDR imaging (e.g.,flare-suppression and compression of highlights).

Upon being tone mapped, the HDR image data may be compressed into SDR orLDR image data via a filtering of the Least-Significant-Bits (LSBs) ofthe HDR pixel values. In some embodiments, prior to the pixel-depthreduction, the tone mapped HDR image data may be color compressed via agamma compression function. For example, HDR image data may be capturedvia fixed (or at least relatively fixed) exposure settings and thecaptured HDR image data may be referred to as source image data. Thetone mapping function may be dynamically determined based on an analysisof pixel values of the source image data. The tone mapping function maybe a non-linear function that maps source pixel values of the sourceimage data to target pixel values of target image data. For non-linearembodiments, the non-linear tone mapping function and/or GTM functionmay be plotted in 2D coordinates as a Global Tone Curve (GTC). The tonemapping function may be used to essentially control the brightness (ortone) of the target image data, without relying on conventional AEsettings and/or a digital gain.

To generate the tone mapping function, a plurality of control points maybe determined based on the dynamic analysis of the source image data.The control points may be defined in a plane spanned by the ranges ofthe source and target pixel values. In some embodiments, the controlpoints may be defined based on a region-of-interest (ROI) of the sourceimage data. The tone mapping function may define a one-to-one non-linearmapping between the values of the source image pixels and the values oftarget image pixels. Thus, the tone mapping function may define (or atleast evaluate to a numerical approximation thereof) a curve in thesource/target plane. The curve may be an approximation of a curve, e.g.,a plurality of piecewise linear segments with varying slopes. That is,the tone mapping function may be a spline function. The spline functionmay include polynomials with a degree greater than 1. In someembodiments, the tone mapping function may be a one-to-one linearmapping function. The control points within the plane may define one ormore constraints on the tone mapping function. In some embodiments, aparameterized tone mapping function may be fit (e.g., the parametersdefining the tone mapping may be selected by minimizing a difference orcost function) based on the one or more constraints. More specifically,the cost function may be defined by the one or more constraints. Forexample, a spline function, with polynomial segments of any degree, maybe fit based on the one or more constraints.

At least a portion of the control points may indicate constraints fortone mappings of a specific and a finite number of source pixel valuesand corresponding target pixel values. The tone mapping function may befit to at least approximate these specific tone mappings. In order tosuppress flares and compress highlights, some of the control points maydefine flare-suppression or highlight compression thresholds for thesource image data. At least some of the control points may be employedto constrain a derivative (or at least a numerical approximationthereof) of the tone mapping function, evaluated at one or controlpoints. That is, some of the control points may be employed to constrainthe slope of the gain (e.g., gain value) of the tone mapping function atone or more other control points.

In some embodiments, at least three control points may be determined: alow-tone point, a mid-tone point, and a high-tone point. The low-tonepoint may define a tone mapping between the lowest pixel value of thesource image data and the lowest pixel value of the target image data,as well as a flare-suppression threshold for the source image data.Similarly, the high-tone point may define a tone mapping between thehighest pixel value of the source image data and the highest pixel valueof target image data, as well as a highlight compression threshold forthe source image data. The mid-tone point may define a tone mappingbetween a mid-tone value of the source image data and a mid-tone valueof the target image data. As discussed below, the mid-tone point may beadditionally employed to constrain the derivative of the tone mappingfunction.

Because the low-tone point may define the mapping between tone valuesfor the darkest or the “black” pixels of the source and target imagedata, the low-tone point may be a “black point” (BP) of the mapping.Likewise, because the high-tone point may define the mapping betweentone values for brightest or “white” pixels of the source and targetimage data, the high-tone point may be a “white point” (WP) of themapping. In some embodiments, when fitting the tone mapping function,the tone mapping parameters may be selected to force the tone mappingfunction to evaluate to (or at least approximate) these control points.The tone mapping function may be constrained to evaluate to (or at leastapproximate) additional control points.

In at least some embodiments, the derivative (or at least the numericalapproximation thereof) of the tone mapping function may be constrainedat the mid-tone point, or any other such control point. That is, theslope of the mid-tone gain (defined via the tone mapping function) maybe constrained and/or set at the mid-tone point. To constrain thederivative (or the numerical approximation thereof) of the tone mappingfunction at the mid-tone point, an additional control point may bedefined. The derivative of the tone mapping function, evaluated at themid-tone point, may be constrained to be at least approximatelyequivalent to the slope of a line (e.g., gain value) passing through themid-tone point and an additional control point. In one such example, theadditional control point may be a maximum flare removal (MFR) point thatspecifies a threshold on the source pixel values in order to removeflairs. The MFR point may be set by a user and/or determined dynamicallybased on statistics of the source image data.

As further examples, the slope of the mid-tone gain may be constrainedto be at least approximately equivalent to the ratio of the mid-tone ofthe target image data to mid-tone of the source image data. As stillfurther examples, the slope of the mid-tone gain may be set by othermethods, e.g., a user configurable setting. When fitting theparameterized tone mapping function, the parameters may be selected toforce the derivative of the tone mapping function, evaluated at themid-tone point, to at least approximate the mid-tone gain, defined inthese or any other manner.

Once the tone mapping function is determined, the target image data maybe generated by applying the tone mapping function to the source imagedata. In some embodiments, there may be a one or more frame offsetbetween the determination of the tone mapping function and theapplication of the tone mapping function. For example, the tone mappingfunction may be determined based on a first source image frame andapplied to a subsequent frame. As noted above, SDR or LDR image data maybe generated from the target image data by filtering an appropriatenumber of LSBs from the target image data. Prior to dropping the LSBsfrom the image data, gamma compression may be applied to the targetimage data to compress the color range of the pixel values.

In some embodiments, the tone mapping function may be determined via ageneral-purpose processor (e.g., a CPU) of an imaging device, while thesource image data may be mapped to the target image data via one or morepipelines of a specialized-processor (e.g., an image signal processor,FPGA, or an ASIC) of the imaging device. In some embodiments, the sourceimage data may be processed by a graphical processing unit (GPU), imagesignal processor (ISP), and/or a digital signal processor (DSP). In someembodiments, statistical metrics of the source image data may bedetermined, and the control points may be determined from thestatistical metrics. Some of the disclosed embodiments may be deployedin vehicle-mounted imaging devices (e.g., dash-cams). Further, thevarious embodiments may be deployed in autonomous vehicle applications,or other such machine-vision applications. The embodiments may bedeployed in any application that employs one or more machine and/orcomputer vision methods. For example, the embodiments may be deployed toenable any of the various machine vision features of an autonomousvehicle (See FIGS. 6A-6D). The embodiments may be deployed to enablemachine vision in a robot, such as but not limited to a manufacturingrobot.

Systems for High-Dynamic-Range Imaging and Tone Mapping

With reference to FIG. 1 , FIG. 1 provides a schematic diagram of aHigh-Dynamic-Range Imaging (HDRI) system 100, in accordance with someembodiments of the present disclosure. It should be understood that thisand other arrangements described herein are set forth only as examples.Other arrangements and elements (e.g., machines, interfaces, functions,orders, groupings of functions, etc.) can be used in addition to orinstead of those shown, and some elements may be omitted altogether.Further, many of the elements described herein are functional entitiesthat may be implemented as discrete or distributed components or inconjunction with other components, and in any suitable combination andlocation. Various functions described herein as being performed byentities may be carried out by hardware, firmware, and/or software. Forinstance, various functions may be carried out by logic device, such asbut not limited to a general purpose processor 122 and/or an imagesignal processor (ISP) 124 executing instructions stored in memory.

The HDRI system 100 may include, among other things, computing devicesthat include one or more image sensors (e.g., a camera). Such computingdevices may include, but are not limited to, a mobile or stationarycamera (e.g., a handheld camera 102, a smartphone, a tablet, or thelike), a manned or unmanned terrestrial vehicle (e.g., a vehicle 104), amanned or unmanned aerial vehicle (e.g., a drone 106), or a wearabledevice (e.g., smart glasses 108). Such computing devices that includeone or more image sensors may herein be referred to collectively as thecamera computing devices 102-108.

Although some camera computing devices are illustrated in FIG. 1 , thisis not intended to be limiting. In any example, there may be any numberof camera computing devices and/or camera computing devices that are notexplicitly shown in FIG. 1 . Virtually any computing device thatincludes one or more image sensors and/or cameras may be included in anHDRI system that is in accordance with the various embodiments.

Any of the camera computing devices 102-108 (or other camera computingdevices included in the system 100) may include one or more imagesensors that are enabled to capture High-Dynamic-Range (HDR) image data,as discussed throughout. The HDRI system 100 may include other computingdevices, such as but not limited to a server computing device 110. Theserver computing device 110 may not include an image sensor. However, inother embodiments, the server computing device 110 may include an imagesensor (e.g., an auxiliary camera). The terrestrial vehicle 104 and/orthe aerial vehicle 106 may be at least partially manually operatedvehicles and/or when manned, partially autonomous. In some embodiments,when unmanned, the vehicles 104 and 106 may be autonomous, partiallyautonomous, and/or remote controlled vehicles. Various embodiments ofsuch vehicles are discussed in conjunction with FIGS. 6A-6D.

Various embodiments of computing devices, including but not limited tothe computing devices 102-110 are discussed in conjunction with acomputing device 700 of FIG. 7 . However, briefly here, each onecomputing devices may include one or more logic devices. For example,the camera 102 is shown to include logic devices 120. The logic devices120 may include one or more of the general purpose processor 122 (e.g.,a Central Processing Unit (CPU), a microcontroller, a microprocessor, orthe like), the Image Signal Processor (ISP) 124, an Application SpecificIntegrated Circuit (ASIC) 126, and/or a Field Programmable Gate Array(FPGA) 128. Although not shown in FIG. 1 , in some embodiments, thelogic devices 120 may include a Graphics Processing Unit (GPU). Itshould be noted that any of the computing devices 102-110 may includeone or more of such logic devices.

Various components of the HDRI system 100 (e.g., the computing devices102-110) may communicate over network(s) 112. The network(s) may includea wide area network (WAN) (e.g., the Internet, a public switchedtelephone network (PSTN), etc.), a local area network (LAN) (e.g.,Wi-Fi, ZigBee, Z-Wave, Bluetooth, Bluetooth Low Energy (BLE), Ethernet,etc.), a low-power wide-area network (LPWAN) (e.g., LoRaWAN, Sigfox,etc.), a global navigation satellite system (GNSS) network (e.g., theGlobal Positioning System (GPS)), and/or another network type. In anyexample, each of the components of the HDRI system 100 may communicatewith one or more of the other components via one or more of thenetwork(s) 112.

Any of the computing devices 102-110 may implement, operate, orotherwise execute the functions and/or operations of a Digital AutoExposure High Dynamic Range (DAE HDR) engine 140. For example, in FIG. 1, the camera 102 is shown implementing the DAE HDR engine 140. However,any of the computing devices 102-110 may be enabled to implement the DAEHDR engine 140. Any of the logic devices 120 may implement at least someof the functions, operations, and/or actions of the DAE HDR engine 140.

The DAE HDR engine 140 may enable various methods of the tone mapping ofHDR image data, as well as controlling the brightness of the imageencoded by HDR image data and/or the tone-mapped image data. To carryout such functionality, the DAE HDR engine 140 may include one or morecomponents, modules, devices, or the like. Such components, modulesand/or devices may include but are not limited to one or more HDR imagesensors 144, HDR sensor exposure settings 142, one or more HDR imagesensors 144, a Region of Interest (ROI) locator 148, a delay unit 150, astatistics module 152, a control points selector 154, a tone mapgenerator 156, and/or a tone map applicator 158. Any of thesecomponents, modules, and/or devices may be optional in some embodiments.For example, in the non-limiting embodiment shown in FIG. 1 , the ROIlocator 148, the delay unit 150, and the statistics module 152 areoptional. Each of the other components, modules, and/or devices may beoptional in other embodiments.

The enumeration of components, modules, and/or devices of the DAE HDRengine 140, as discussed in conjunction with FIG. 1 , is not intended tobe exhaustive. In other embodiments, the DAE HDR engine 140 may includefewer or more components, modules, and/or devices. As discussedthroughout, the DAE HDR engine 140 may implement and/or carry out atleast portions of the processes, actions, and/or operations discussed inconjunction with the methods 300, 400, and 500 of FIGS. 3, 4, and 5respectively. As such, one or more of the logic devices 120 mayimplement and/or carry out at least portions of the methods 300, 400,and/or 500.

The HDR image sensors 144 are enabled to capture image data that is HDRimage data. The captured HDR image data encodes an image or scene thatis imaged by the HDR image sensors 144. The image data captured by theHDR image sensors 144 may be referred to as source image data. Thus,source image data may be HDR image data that encodes an HDR sourceimage. As discussed throughout, the HDR image sensors 144, which capturesource image data, may be mounted on a vehicle (e.g., the terrestrialvehicle 104 or the aerial vehicle 106). The vehicle may be anautonomous, or at least a partially autonomous, vehicle. The vehicle maybe controlled, at least partially, based on the source image data and/orthe target image data. In some embodiments, the encoding of the sourceimage data may be in a linear color space that lacks a non-linearmapping. The HDR image sensors 144 may include, be affected by, and/orbe subject to one or more HDR sensor exposure settings 142. The HDRsensor exposure settings 142 may be fixed, static, and/or constantexposure settings. In other embodiments, at least a portion of the HDRsensor exposure settings 142 may be dynamic and/or variable. In suchembodiments, at least a portion of the values of the HDR sensor exposuresettings 142 may be automatically determined based on the lightingconditions of the scene to be imaged and/or other environmentalconditions. The HDR sensor exposure settings 142 may be automaticallyset based on these and other factors, such as the specifications and/oroperations of the HDR image sensors 144. In at least one embodiment, auser may be enabled to manually set at least a portion of the HDR sensorexposure settings 142.

Some computing devices in the HDRI system 100 may not include imagesensors and/or cameras (e.g., the server computing device 110). In suchembodiments, the HDR image sensors included in any of the cameracomputing devices 102-108 may employed to capture the source image data.The source image data may be provided, via networks 112, to a DAE HDRengine that is implemented at and/or by the server computing device 110.That is, although the source HDR image data may be captured by at leastone of the camera devices 102-108, the tone mapping and controlling thebrightness of the image data may be performed offline on the servercomputing device 110. To state in another fashion, the tone mapping ofthe HDR image data may be offloaded to another computing device, such asbut not limited to the server computing device 110, which did notcapture the image data. Because the camera computing devices 102-108 mayinclude one or more manned or unmanned vehicles (e.g., the terrestrialvehicle 104 and aerial vehicle 106), the source image data may becaptured by a camera included in or mounted on a vehicle. As notedabove, the vehicle may be an autonomous, or at least partiallyautonomous, vehicle. The various embodiments may enable, or at leastassist in the enablement, in various machine and/or computer visionfeatures of an autonomous vehicle, such as but not limited toterrestrial vehicle 104 or aerial vehicle 106. The embodiments may bedeployed to enable the machine and/or computer vision features of otherapplications, such as but not limited to robotic applications.

As shown in FIG. 1 , the DAE HDR engine 140 includes two parallelpipelines for the source image data, as indicated by the arrows. Morespecifically, the DAE HDR engine 140 includes an image data pipeline 162and an image data pipeline 164, where the two pipelines may be operatedin parallel. The two pipelines schematically bifurcate between the HDRimage sensors 144 and the optional ROI locator 148. The two forkedpipelines schematically converge at the tone map applicator 158.

The image data pipeline 162 is generally responsible for determiningand/or generating the tone mapping function (e.g., a Global Tone Mapping(GTM) function). The image data pipeline 162 (via either the optionaldelay unit 150 or the tone map generator 156) provides the tone mappingfunction to the image data pipeline 164 via the tone map applicator 158.The image data pipeline 164 is generally responsible for applying thetone mapping function to the source image data to generate target imagedata 160. As discussed below, the one or more HDR image sensors 144capture source image data, and provides the source image data to each ofthe image data pipelines 162 and 164. In the non-limiting embodiment ofFIG. 1 , and as shown via the pipeline flow arrows, the source imagedata is provided to the image data pipeline 162 via the optional ROIlocator 148 and the source data is provided to the parallel image datapipeline 164 via the HDR image data buffer 146.

In embodiments that involve the capturing of multiple frames of sourceimage data (e.g., HDR video embodiments), the tone mapping function maybe generated based on a first frame of source image data and applied toa second (e.g., the next consecutive) frame of source image data. Thatis, in such embodiments, it may be assumed that the tone mappingfunction generated based on the first frame of source image data isapplicable to and appropriate for the next frame of source image databecause the environment (e.g., lighting) conditions are not varyingsignificantly from frame to frame. In these embodiments, there may be aone frame lag between the source data that the tone mapping function wasgenerated from and the source image data that the tone mapping functionis applied to. For example, the frame of the source image data that wasemployed to generate the tone mapping function may be one frame previousto the frame of the source image data that the tone mapping function wasapplied to. In such embodiments, the optional delay unit 150 of theimage data pipeline 162 may buffer the tone mapping function for one (ormore) frames, such that when the tone mapping function is provided tothe tone map applicator 158 of the image data pipeline 164, the tone mapis applied to the next consecutive frame of source image data. In otherembodiments, the lag may be greater than a single frame, and the delayunit 150 may buffer the tone mapping function for multiple frames ofsource image data. In at least one embodiment, the same tone mappingfunction may be applied to more than a single frame of source imagedata. For example, the same tone mapping function may be applied to fiveconsecutive frames of source image data. In such embodiments, the imagedata pipeline 162 may generate a tone mapping function for only everyfifth frame.

As shown in FIG. 1 , the DAE HDR engine 140 outputs the target imagedata 160. As discussed throughout, the target image data 160 may encodethe image encoded by the source image data. However, rather than thepixel values of the source image data captured by the HDR image sensors144, the pixel values of the target image data 160 may be defined byapplying (via the image data pipeline 164) the tone mapping function(determined via the image data pipeline 162) to the source image data.That is, the pixel values of the target image data 160 may berepresentative of a tone-mapped version of the pixel values of thesource image data. In some embodiments, the outputted target image data160 may be either Standard Dynamic Range (SDR) image data or Low DynamicRange (LDR) image data. However, in other embodiments, the target imagedata 160 may be HDR image data. Since the pipelines are at leastsomewhat parallel pipelines, and in some embodiments, at least a portionof the operations of the image data pipeline 162 may be performed by afirst logic device (e.g., the general purpose processor 122) and atleast a portion of the operations of the image data pipeline 164 may beperformed by a second logic device (e.g., the ISP 124). In at least oneembodiment, one or more pipelines within the ISP 124 may be employed bythe image data pipeline 164 of the DAE HDR engine 140.

As shown in FIG. 1 , at least a portion of the source image data may beprovided and/or received by the image data pipeline 164 via an HDR imagedata buffer 146. The HDR image data buffer 146 may buffer, or at leasttemporarily store, the source image data. As discussed in more detailbelow, the image data pipeline 162 generates a tone mapping function andprovides the tone mapping function to the image data pipeline 164. Morespecifically, as shown in FIG. 1 , the optional display unit buffers thetone mapping function for at least one frame, and then provides the tonemapping function to the tone map applicator 158 of the image datapipeline 164. The tone map applicator receives source image data fromthe HDR image data buffer 146 and applies the tone mapping function tothe source image data to generate the target image data 160.

As noted above, the source image data received by the tone mapapplicator 158 may be a next frame of source image data, as compared tothe frame in source image data that was employed to generate the tonemapping function. In embodiments that do not include the delay unit 150,the source image data may be provided to the tone map applicator 158directly from the tone map generator 156. In such embodiments, the tonemapping function may be applied to the same frame of source image datathat was employed to generate the tone mapping function.

In embodiments that include the optional ROI locator 148, the sourceimage data may be provided to and/or received by the image data pipeline162 via ROI locator 148. In embodiments that do not include the ROIlocator 148, but do include the statistics module, the source image datais provided to the image data pipeline 162 via the optional statisticsmodule 152. In embodiments that lack both the ROI locator 148 and thestatistics module 152, the source image data may be provided to theimage data pipeline 162 via the control points selector 154. It shouldbe noted that while embodiments may include either ROI locator and/orthe statistics module 152, their operability may be optional. Forexample, a user may choose to enable the operability of one or both ofthe ROI locator 148 and/or the statistics module 152 via one or moresoftware switches and/or flags. Likewise, the user may choose to disablethe operability of one or both of the ROI locator 148 and/or thestatistics module 152 via the one or more software switches and/orflags.

In embodiments that include and/or enable the operability of the ROIlocator 148, the ROI locator 148 may determine an ROI within the sourceimage data. For example, one or more methods relating to computer visionand/or image processing (e.g., the ROI may be an output of a neuralnetwork trained to identify the ROI) may be employed to determine aninteresting region (e.g., the region of the image that includes thesubject and/or focus point of the image) within the image encoded by thesource image data. As used herein, an ROI may be a region within theimage that includes more contrast, detail, and/or more varied pixelvalues than other regions. For example, the ROI may be a region in theimage, where the dynamic range of the pixel values is maximized, or atleast increased, as compared to other regions in the image.

In embodiments, the ROI may be a region of the image that includes orcorresponds to the subject of the image or the point of focus of theimage. In some embodiments, the ROI locator 148 may include a filter ormask that masks away the pixels outside of the determined ROI. Thus,when the image data travels down the image data pipeline 162, the imagedata may include only the pixel values that correspond to the ROI. Thus,a determination of the control points and the generation of the tonemapping function, as well as other operations of the image data pipeline162 (e.g., a determination of statistical metrics and/or a determinationof a plurality of control points) may be based on the portion of thesource image data that corresponds to the ROI in the encoded sourceimage, rather than the entirety of the source image data that encodesthe source image.

In embodiments that include and/or enable the operability of thestatistics module 152, the statistics module 152 may determine and/orgenerate a plurality of statistical metrics based on the pixel values ofthe source image data (or the pixel values of the portion of the sourceimage data that corresponds to the ROI of the encoded source image). Theplurality of statistical metrics may include statistical metrics thatare based on the pixel values of the source image data. The statisticalmetrics may include one or more parameters that characterize virtuallyany continuous or discrete statistical distribution and/or histogramthat may be constructed from the source image data. Such parameters mayinclude a mean, median, and/or standard deviation of one or morestatistical distributions derived from the pixel values of the sourceimage data.

The source image data, the portion of the source image data thatcorresponds to the ROI, and/or the plurality of statistical metrics maybe provided to the control points selector 154. The control pointsselector 154 is generally responsible for determining a plurality oftone control points based on the source image data, the portion of thesource image data that corresponds to the ROI, and/or the plurality ofstatistical metrics. More particularly, at least a portion of the tonecontrol points may be determined based on pixel values of the sourceimage data, the statistical metrics determined and/or derived from thepixel values, or a combination thereof. Control points selector 154 mayemploy the general purpose processor 122 to determine the plurality oftone control points.

The plurality of control points may include one or more of a low-tonepoint, a mid-tone point, and a high-tone point. The plurality of controlpoints may include a flare-suppression point. In some embodiments, theplurality of tone control points may include additional tone controlpoints. A tone control point may be a 2D point and/or a 2D vector, whichincludes two scalar values (e.g., an x-component and a y-component),although other dimensions could be added. Thus, a tone control point maybe represented via the vector notation (TC_x, TC_y), where each of theTC_x and TC_y are scalar values. The abscissa scalar value (e.g., thex-component and/or x-value) of the tone control point is indicated asTC_x. The ordinate scalar value (e.g., the y-component and/or y-value)of the tone control point is indicated as TC_y. The 2D space that thecontrol points are embedded within may be spanned by an orthonormalbasis that includes an abscissa basis vector (e.g., the x-axis)corresponding the pixel values of the source image data and an ordinatebasis vector (e.g., the y-axis) corresponding to the pixel values of thetarget image data.

The low-tone, mid-tone, and high-tone control points may indicatespecific mappings of the pixel values of the source image data to thepixel values of the target image data. For example, the low-tone pointmay indicate the pixel value of the source image data that is to be tonemapped to the lowest pixel value (e.g., the pixel value that correspondto the darkest or black pixels) of the target image data. Likewise, thehigh-tone point may indicate the pixel value of the source image datathat is to be tone mapped to the highest pixel value (e.g., the pixelvalue that corresponds to the brightest or white pixels) of the targetimage data. As such, the low-tone point may be referred to as the blackpoint (BP) and the high-tone point may be referred to as the white point(WP). The mid-tone point may indicate the pixel value of the sourceimage data that is to be tone mapped to a middle pixel value of thetarget image data. The determination of the mid-point may control theoverall mid-tone brightness (or tone) of the target image encoded by thetone mapped target image data, while the low-tone point controls thetone of the blackest (or darkest) pixels in the target image data andthe high-tone point controls the tone of the whitest (or brightest) ofthe pixels in the target image data.

Referring to FIG. 2A, FIG. 2A shows the low-tone, mid-tone, high-tone,and flare-suppression control points embedded in the 2D space spanned bya first basis vector for a first dimension corresponding to the pixelvalues of the source image data (e.g., the x-axis) and a second basisvector for a second dimension corresponding to the pixel values of thetarget image data (e.g., the y-axis). In the non-limiting embodiment ofFIG. 2A, the pixel values of the source and target image data have beennormalized to have a range of: [0, 1]. However, in other embodiments,the pixel values may be normalized to other ranges, or need not even benormalized. For example, the raw pixel values of the captured image datamay be used as the source image data. In other embodiments, the rawpixel values may be normalized and/or pre-processed prior to beingprovided to the image data pipelines 162 and 164 of the DAE HDR engine140.

In FIG. 2A, the low-tone point is indicated as: LT=(B_s, B_t), themid-tone point is indicated as: MT=(M_s, M_t), and the high-tone pointis indicated as: HT=(W_s, W_t), where the x and y components are allnon-negative scalar values. More specifically, in the non-limitingembodiment of FIG. 2A, LT=(B_s, 0) and HT=(W_s, 1), where0.0<B_s<W_s<1.0. In other examples, B_t need not be equal to 0.0 and W_tneed not be equal to 1. FIG. 2A shows another control point, theflare-suppression point, indicated as: FS=(F_s, F_t), where F_t is setto 0.0. The flare-suppression point is discussed further below.

With regards to the mid-tone point, any pixel in the source image datawith the pixel value equivalent to M_s may be tone mapped, via a tonemapping function, to the value of M_t for the target image data. Thedetermination and/or selection of M_t controls the mid-tone brightnessof the target image. Thus, the determination of M_t may be based on amid-tone pixel value for the pixel values of the target image data. Insome embodiments, M_t=0.5. In other embodiments, M_t may take on othervalues. In some examples, a user may select or set a value for M_t. Infurther examples, M_s may be determined via a linearly-weighted averageof the pixel values of the source image data. In additional examples,M_s may be determined via a logarithmic averaging (e.g., log-averaging)of the pixel values of the source image data. The log-averaging may beperformed in virtually any base. However, in some embodiments the logbase is base 10. In other embodiments, the logarithm function employedto transform the source image data to the log values may be the naturallogarithm function. The log-averaged value of the pixel values may thenbe exponentiated (via the corresponding base) to determine M_s. Forexample, log-transformed source image data pixel values may bedetermined based on the pixel values of the source image data. Anaverage value of the log-transformed image data values may be determinedvia a linearly-weighted sum of the log-transformed image data values.M_s may be determined based on an exponentiation of the averaged valueof the log-transformed image data values.

In some embodiments, only a portion of the source image data is employedto determine M_s. For instance, the pixels of the source image data withthe highest and the lowest values may be vetoed and/or filtered from theanalysis. That is, a high-tone threshold (or filter value) may beemployed to veto the high-tone pixels from the determination of M_s.Likewise, a low-tone threshold (or filter or filter value) may beemployed to veto the low-tone pixels from the determination of M_s. M_smay be determined based on a linear- or log-averaging of the pixelvalues that pass both the low-tone and high-tone filters (e.g., thepixel values that are not thresholded from the analysis). The thresholdsfor the filters may be relative thresholds (e.g., percentages), orabsolute values. In some embodiments, M_s and/or M_t may be determinedbased at least in part on the statistical metrics generated by thestatistics module 152 of FIG. 1 . Any of the various methods discussedabove, with respect to determining M_s and/or M_t may be combined withthe statistical metrics to determine M_s and/or M_t. In at least oneembodiment, M_s and/or M_t may be determined, based at least in part onthe HDR sensor exposure settings 142 of FIG. 1 . A prediction model forM_s and/or M_t may be determined based on the analysis of historical,training, and/or learning data generated by aggregating the statisticalmetrics from large numbers of source image data and/or target imagedata.

With regards to the low-tone point, any pixel in the source image datawith the pixel value equivalent to B_s (or less than B_s) may be tonemapped to the value of B_t for the target image data. That is, eachpixel value of the source image data that is less than B_s may beclipped and set to have a pixel value of B_s. The determination and/orselection of B_t controls the low-tone brightness of the target image.Thus, the determination of B_t may be based on a minimum pixel value forthe pixel values of the target image data. In some embodiments, B_t=0.In other embodiments, B_t may take on a positive value that is less thanM_t. In some examples, a user may select or set a value for B_t. Apositive black pixel value may be caused by a flare, or other errors(e.g., a sensor black level subtraction error), in the image sensor thatcaptured the source image data for the pixel. Thus, because source imagedata with pixel values less than B_s are clipped and set to B_s, theselection of B_s may control flare suppression. Accordingly, B_s may bereferred to as a flare-suppression threshold.

In examples, B_s may be determined based on the pixels of the sourceimage data with the lowest pixel values. For instance, a low-tone subsetof the pixel values of the source image data may be determined based ona low-tone point threshold. Every pixel value included in the low-tonesubset may be less than or equal to the low-tone point threshold. Everypixel value excluded from the low-tone subset may be greater than thelow-tone point threshold. The low-tone point threshold may be either anabsolute or a relative threshold. The value of B_s may be determinedbased on the pixel values included in the low-tone subset of pixelvalues. For example, B_s may be set to the weighted average of the pixelvalues in the low-tone subset may be averages. In another embodiment,B_s may be set to a predetermined percentage of the pixel values in thelow-tone subset. In some embodiments, B_s and/or B_t may be determinedbased at least in part on the statistical metrics generated by thestatistics module 152 of FIG. 1 . Any of the various methods discussedabove, with respect to determining B_s and/or B_t may be combined withthe statistical metrics to determine B_s and/or B_t. In at least oneembodiment, B_s and/or B_t may be determined, based at least in part onthe HDR sensor exposure settings 142. A prediction model for B_s and/orB_t may be determined based on the analysis of training and/or learningdata generated by aggregating the statistical metrics from large numbersof source image data and/or target image data.

With regards to the high-tone point, any pixel in the source image datawith the pixel value equivalent to W_s (or greater than W_s) may be tonemapped to the value of W_t for the target image data. That is, eachpixel value of the source image data that is greater than W_s may beclipped and set to have a value of W_s. Thus, because source image datawith pixel values greater than W_s are clipped and set to W_s, theselection of W_s may control highlight (e.g., pixels with large pixelvalues) suppression. Accordingly, W_s may be referred to as ahighlight-suppression threshold. The determination and/or selection ofW_t controls the high-tone brightness of the target image. Thus, thedetermination of W_t may be based on a maximum pixel value for the pixelvalues of the target image data. In some embodiments, W_t=1. In otherembodiments, W_t may take on a positive value that is less than 1 butgreater than M_t. In some examples, a user may select or set a value forW_t. In additional examples, W_s may be determined based on the pixelsof the source image data with the highest pixel values. For instance, ahigh-tone subset of the pixel values of the source image data may bedetermined based on a high-tone point threshold. Every pixel valueincluded in the high-tone subset may be greater than or equal to thehigh-tone point threshold. Every pixel value excluded from the high-tonesubset may be less than the high-tone point threshold.

The high-tone point threshold may be either an absolute or a relativethreshold. The value of W_s may be determined based on the pixel valuesincluded in the high-tone subset of pixel values. For example, W_s maybe set to the weighted average of the pixel values in the high-tonesubset may be averages. As another example, W_s may be set to apredetermined percentage of the pixel values in the high-tone subset. Insome embodiments, W_s and/or W_t may be determined based at least inpart on the statistical metrics generated by the statistics module 152of FIG. 1 . Any of the various methods discussed above, with respect todetermining W_s and/or W_t may be combined with the statistical metricsto determine W_s and/or W_t. In at least one embodiment, W_s and/or W_tmay be determined, based at least in part on the HDR sensor exposuresettings 142. A prediction model for W_s and/or W_t may be determinedbased on the analysis of training and/or learning data generated byaggregating the statistical metrics from large numbers of source imagedata and/or target image data.

FIG. 2A also shows a flare-suppression point: FS=(F_s, 0). F_s mayindicate a maximum flare removal threshold. In some embodiments, F_s maybe user specified and/or selected. In other embodiments, F_s may bedynamically determined based at least in part on the statistical metricsof the source image data. In at least one embodiments, F_s may bedetermined based on a percentage of M_s and/or a value of a percentageof the lowest pixel values of the source image data.

Returning to FIG. 1 , the control points selector 154 may determine oneor more additional tone control points. The additional tone controlpoints may be determined based on, at least in part, the plurality ofstatistical metrics. Tone map generator 156 is generally responsible fordetermining the tone mapping function based on the plurality of controlpoints. Tone map generator 156 may employ the general purpose processor122 to determine the tone mapping function. To determine the tonemapping function, the tone map generator 156 may generate and/ordetermine a gain line. The generation of the gain line may be based onat least a portion of the plurality of tone control points. A gain valuemay be determined as the slope, derivative, and/or rate of change of thegain line. In some embodiments, the gain line may be determined as theunique line that includes, or passes through, at least two of thecontrol points. In the example shown in FIG. 2A, the gain line is theline that includes both the mid-tone point and the flare-suppressionpoint. The gain value is equivalent to the slope of the gain line.

Tone map generator 156 may determine the tone mapping function based onthe gain value and at least a portion of the plurality of tone controlpoints. The tone mapping function may map a pixel value of the sourceimage data to a pixel value of the target image data. As such, the tonemapping function may be a scalar function of a single scalar variable(e.g., a pixel value), where the value of the function is the pixelvalue of the target image data that corresponds to the pixel value ofthe source image data that is the argument (or independent variable) ofthe function. The mapping may be a non-linear mapping. In someembodiments, the tone map generator 156 may perform a fit of the tonemapping function to one or more of the tone control points. The tonemapping function may be constrained to include or approximately includeone or more of the tone control points. For example, in at least oneembodiment, the tone mapping function may be constrained to include thelow-tone point, the mid-tone point, and/or the high-tone point. In someembodiments, the tone mapping function may be constrained by the gainvalue. The derivative, or instantaneous rate of change, of the tonemapping function (evaluated at one or more of the tone control points)may be constrained based on the gain value. For example, in at least oneembodiment, the fitting of the tone mapping function may be constrainedsuch that the derivative, or instantaneous rate of change of a tangentline at the mid-tone control point is at least approximately equivalentto the gain value.

Turning to FIG. 2B, FIG. 2B shows a non-limiting example of a plot of atone mapping function, which is in accordance with the variousembodiments. The tone mapping function of FIG. 2B is constrained suchthat the plot of the tone mapping function includes the low-tone point,the mid-tone point, and the high-tone point. The tone mapping functionis further constrained such that the derivative or instantaneous rate ofchange of a tangent line, at the mid-tone control point is equivalent tothe gain value. It should be noted that the tone mapping function may befurther constrained based on additional tone control points. FIG. 2Balso shows the corresponding low-tone point, the mid-tone point, thehigh-tone point, and the gain line.

To determine the tone mapping function, one or more parametric functionsmay be fit, wherein the fit is constrained by at least a portion of theplurality of tone control points. The parametric functions may includeone or more polynomials, of virtually any degree. In some embodiments,the fitting of the tone mapping function may be constrained, such thatthe tone mapping function includes and/or intersects with at least thelow-tone point, the mid-tone point, and the high-tone point. In stillfurther embodiments, the fitting of the tone mapping function may beconstrained, such that the tone mapping function includes and/orintersects the additional tone control points. In some embodiments, thefitting of the tone mapping function is constrained, such that thederivative and/or the instantaneous rate of change of the tone mappingfunction, evaluated at the x-component of the mid-tone points isequivalent to the gain value.

Various spline methods may be employed to fit and/or generate the tonemapping function. Generating the tone mapping function may includegenerating and/or constructing a non-linear curve. The non-linear curvemay be a global tone curve (GTC). The curve may include a plurality oflinear or curved segments (e.g., a plurality of splines). The curve maybe a Bezier curve, e.g., a quadratic or a cubic Bezier curve. The curvemay be constructed via second-, third, or higher order parametricequations. Various spline methods may be employed to generate the curve.The joint between the splines or segments could be constructed to ensurethat the derivative of the tone mapping function is continuous.

Returning to FIG. 1 , in embodiments that include the optional delayunit 150 (or the functionality of the delay unit 150 is engaged), thedelay unit 150 may buffer the tone mapping function, while the HDR imagesensors 144 capture one or more additional frames of source image data.Upon the capturing of the one or more additional frames of source imagedata, the delay unit 150 may provide the buffered tone mapping functionto the image data pipeline 164 of the DAE HDR engine 140, via the tonemap applicator 158. Where a frame lag is used between the frame ofsource image data that is employed to determine the tone mappingfunction and the frame of source image data that the tone mappingfunction is applied to, the delay unit 150 may not be required. Forexample, the next subsequent and/or successive frame of source imagedata may be provided to the HDR image data buffer 146 and/or the tonemap applicator 158, by the time that the tone map generator 156 canprovide the tone mapping function to tone map applicator. Where thedelay unit 150 is not used, the tone map generator 156 may provide thetone mapping function directly to the tone map applicator 158.

Tone map applicator 158 may receive the source image data and the tonemapping function. Tone map applicator 158 may apply the tone mappingfunction to the source image data to generate the target image data 160.That is, tone map applicator may transform the source image data (e.g.,either the frame of source image data that was employed to generate thetone mapping function and/or one or more subsequent frames of sourceimage data) to generate the target image data 160. Tone map applicator158 may employ the ISP 124 to apply the tone mapping function to thesource image data. In some embodiments, a pipeline of the ISP 124 may beemployed to apply the tone mapping function to the source image data. Asnoted above, the tone mapping function may provide a non-linear mappingof the pixel values of the source image data to the pixel values of thetarget image data 160. In some embodiments, the mapping may be aone-to-one mapping. In other embodiments, the mapping may not be aone-to-one mapping. For instance, in embodiments where the x-componentof the low-tone point is greater than 0.0 and/or where the x-componentof the high-tone point is less than one, the source image data may beclipped via the corresponding x-components. In such embodiments, themapping may not be a one-to-one mapping.

In some embodiments, the tone map applicator 158 may transform the tonemapped target image data into SDR or LDR target image data. In suchembodiments, a gamma-compression function may be applied to the tonemapped target image data to generate color-compressed target image data.Either SDR or LDR target image data may be outputted by the DAE HDRengine 140 based on the color-compressed target image data.

Methods for High-Dynamic-Range Imaging and Tone Mapping

Now referring to FIGS. 3-5 , each block of the methods 300, 400, and 500described herein, comprises a computing process that may be performedusing any combination of hardware, firmware, and/or software. Forinstance, various functions may be carried out by a processor executinginstructions stored in memory. The methods may also be embodied ascomputer-usable instructions stored on computer storage media. Themethods may be provided by a standalone application, a service or hostedservice (standalone or in combination with another hosted service), or aplug-in to another product, to name a few. In addition, the methods 300,400 and 500 are described, by way of example, with respect to the HDRIsystem 100 of FIG. 1 . For example, at least portions of the methods300, 400, and 500 may be carried out by the DAE HDR engine 140 and/orone or more of the logic devices 120A. However, these methods mayadditionally or alternatively be executed by any one system, or anycombination of systems, including, but not limited to, those describedherein.

FIG. 3 is a flow diagram showing the method 300 for tone mapping highdynamic range image data, in accordance with some embodiments of thepresent disclosure. The method 300 begins at block B302, where sourceimage data is captured by one or more image sensors (e.g., the HDR imagesensors 144 of FIG. 1 ). The source image data may be HDR image data.The source image data may be a first frame of source image data. In atleast some embodiments (e.g., video embodiments), one or more additionaland/or consecutive frames of source image data (e.g., a secondconsecutive frame of source image data) may be captured subsequent tothe first frame of source image data. The source image data may encode asource image. If additional frames of source image data are captured,the additional frames of source image data may encode one or moreadditional source images. Thus, capturing source image data at blockB302 may include capturing one or more frames of source image data.

The source image data may be captured by at least one image sensor(e.g., of a camera device) that is mounted on a manned or unmannedterrestrial or aerial vehicle (e.g., the terrestrial vehicle 104 and/oraerial vehicle 106 of FIG. 1 ). The vehicle may be a manually operatedvehicle, an autonomous vehicle, a partially autonomous vehicle, and/or aremote controlled vehicle. The vehicle may be operated based on thesource image data. In at least some embodiments, the vehicle may becontrolled at least partially based on the target image data, generatedat block B314. However, image sensors described herein may be part ofany suitable device, such as a handheld or stationary camera. In atleast one embodiment, the image sensors may be included in one or morerobots.

At block B302, the source image data may be received at and/or providedto a Digital Auto Exposure High Dynamic Range (DAE HDR) engine, such asbut not limited to the DAE HDR engine 140 of FIG. 1 . In at least oneembodiments, the source image data may be provided to and/or received byat least one of: the HDR image data buffer 146, the ROI locator 148, thestatistics module 152, and/or the control points selector 154 of the DAEHDR engine 140.

At optional block B304, a Region of Interest Filter (ROI) filter may beapplied to the received source image data. For example, the ROI locator148 of FIG. 1 may determine an ROI of the source image data. The ROIlocator 148 may apply a filter and/or mask to the source image datapixels corresponding to the ROI of the source image such that thefiltered source image data includes only image data corresponding to thedetermined ROI of the source image. Note that block B304 is optional,and the source image data need not be filtered and/or analyzed based onan ROI.

At optional block B306, one or more statistical metrics may be generatedand/or determined from the filtered (or unfiltered) source image data.For example, the statistics module 152 of FIG. 1 may determine and/orgenerate a plurality of statistical metrics based on the pixel values ofthe source image data (or the pixel values of the portion of the sourceimage data that corresponds to the ROI of the encoded source image).

At block B308, a plurality of tone control points are determined for thesource image data. For example, the control points selector 154 of theDAE HDR engine 140 may determine and/or select a low-tone point, amid-tone point, and/or a high-tone point based on the source image data.In embodiments where the source image data was filtered based on an ROI,the tone control points may be determined based on the portion of thesource image data that correspond to the ROI of the source image. Inembodiments where a plurality of statistical metrics were determined atblock B306, at least a portion of the tone control points may bedetermined based at least partially on at least a portion of thestatistical metrics. In at least some embodiments, additional controlpoints may be determined at block B308. For example, at least aflare-suppression control point may additionally be determined at blockB308. Various embodiments of determining a plurality of control pointsare discussed in conjunction with at least the method 400 of FIG. 4 .Further embodiments of determining a low-tone point, a mid-tone point, ahigh-tone point, and a flare-suppression point are discussed inconjunction with FIG. 2A.

At block B310, a tone mapping function may be determined based on thetone control points. For example, the tone map generator 156 of FIG. 1may determine and/or generate a tone mapping function based on the tonecontrol points. Thus, the tone mapping function may be based on thesource image data that corresponds to the ROI of the source image and/orthe plurality of statistical metrics of the source image data.

Various embodiments of determining a tone mapping function are discussedin conjunction with at least FIGS. 2A-2B and FIG. 4 . However, brieflyhere, a gain line may be determined based on at least the mid-tone pointand the flare-suppression point. A gain value may be determined based onthe gain line. More specifically, the gain value may be at leastapproximately equivalent to the slope of the gain line, which is theline that includes both the mid-tone point and the flare-suppressionpoint. The tone mapping function may be based at least on the low-tonepoint, the mid-tone point, the high-tone point, and the gain value. Forexample, the tone mapping function may be a fitted function that isconstrained to include and/or pass through each of the low-tone point,the mid-tone point, and the high-tone point.

In at least one embodiment, the fitting of the tone mapping function isconstrained such that the derivative and/or instantaneous rate of changeof the tone mapping function, when evaluated at the mid-tone point, isat least approximately equivalent to the gain value. In at least oneembodiment, a first logic device (e.g., the general purpose processor122 of FIG. 1 ) may be employed to determine the tone mapping function.In at least one embodiment, the logic device employed to determineand/or generate the tone mapping function may be a general purposeprocessor of the camera computing device that included the image sensorsthat captured the source image data.

As noted throughout, the tone mapping function provides a mapping fromthe pixel values of the source image data to the pixel values of thetarget image data. Thus, the tone mapping function may be a scalarfunction, which is dependent upon a single scalar variable, e.g., thescalar value of a single pixel of the source image data. The scalarvalue of the function, as evaluated at the scalar pixel value of thesource image data, may be the tone mapped scalar value of the targetimage data for the corresponding pixel. As noted throughout, the mappingmay be a one-to-one non-linear mapping. Because the tone mappingfunction may be constrained to include the low-tone point, the tonemapping function may map a pixel of the source image that has the scalarvalue of the x-component of the low-tone point to the scalar value ofthe y-component of the low-tone point.

In some embodiments, any pixel of the source image data that has a valueless than the x-component of the low-tone point may be clipped, suchthat the value of the clipped pixel is set to the x-component of thelow-tone point. In at least one embodiment, any pixel of the sourceimage data that has a value less than the x-component of the flaresuppression point may be clipped, such that the value of the clippedpixel is set to the x-component of the flare-suppression point. Becausethe tone mapping function may be constrained to include the mid-tonepoint, the tone mapping function may map a pixel of the source imagethat has the scalar value of the x-component of the mid-tone point tothe scalar value of the y-component of the mid-tone point. Similarly,because the tone mapping function may be constrained to include thehigh-tone point, the tone mapping function may map a pixel of the sourceimage that has the scalar value of the x-component of the high-tonepoint to the scalar value of the y-component of the high-tone point. Insome embodiments, any pixel of the source image data that has a valuegreater than the x-component of the high-tone point may be clipped, suchthat the value of the clipped pixel is set to the x-component of thehigh-tone point. One non-limiting embodiment of a non-linear tonemapping function is shown in FIG. 2B.

At optional block B312, a frame delay may be employed. For example, thedelay unit 150 of FIG. 1 may buffer the tone mapping function, while theHDR image sensors 144 capture one or more additional frames of sourceimage data (e.g., a subsequent and/or consecutive second frame of sourceimage data). The frame delay of block B312 may include buffering thefirst frame of image data (that was employed to generate the tonemapping at block B310), while the second frame of image data is beingcaptured, or at least until the second frame of image data is providedto the DAE HDR engine.

At block 314, target image data may be generated based on the sourceimage data and the tone mapping function. For example, the tone mapapplicator 158 may apply the tone mapping function to source image data.The target image data may encode a target image, where the pixel valuesof the target image data are defined by the tone mapping function beingapplied to the source image data. Applying the tone mapping function tothe source image data may include applying the tone mapping function tothe pixel values of the source image data. Applying the tone mappingfunction to the source image data may include transforming and/ormapping the source image data to the target image data via thenon-linear and one-to-one mapping and/or correspondence between sourceand target image data provided by the tone mapping function. Inembodiments that include a frame delay, the tone mapping function may beapplied to the frame of source image data (e.g., a second frame ofsource image data) that is subsequent and/or consecutive to the frame ofsource image data that was employed to generate the tone mappingfunction (e.g., a first frame of source image data).

In embodiments where the frame delay of block B312 is not employed, thetone mapping function may be applied to the same frame of source imagedata that was employed to determine the tone mapping function. In someembodiments, a second logic device (e.g., the Image Signal Processor(ISP) 124 of FIG. 1 ) may be employed to apply the tone mapping functionto the source image data to generate the target image data. In someembodiments the employed ISP may be an ISP of the camera computingdevice that was employed to capture the source image data. In at leastone embodiment, a pipeline of the ISP may be employed to apply the tonemapping function to the source image data.

In some embodiments, generating target image data may include generatingStandard Dynamic Range (SDR) or Low Dynamic Range (LDR) target imagedata. The SDR or LDR target image data may be based on the tone mappingfunction and/or the pixel values of the tone mapped target image data.For example, a gamma compression function may be applied to the tonemapped target image data to generate color-compressed target image data.SDR or LDR target image data may be generated based on thecolor-compressed target image data.

FIG. 4 is a flow diagram showing the method 400 for generating lowerdynamic range image data from higher dynamic range image data, inaccordance with some embodiments of the present disclosure. BlocksB402-B410 of the method 400 include selecting and/or determining aplurality of tone control points. As noted throughout, the plurality oftone control points may be selected and/or determined via the controlpoints selector 154 of FIG. 1 . Various embodiments of determining theplurality of control points are discussed in conjunction with at leastblock B308 of the method 300 of FIG. 3 . The determination of theplurality of control tone points may be based on the pixel values of thesource image data, the pixel values of the portion of the source imagedata that corresponds to the ROI in the source image, and/or theplurality of statistical metrics that are based on the pixel values ofthe source image data. As also noted throughout, the plurality of tonecontrol points may include at least a low-tone point, a mid-tone point,and/or a high-tone point. In some embodiments, the plurality of tonecontrol points may additionally include a flare-suppression point. Suchtone control points are shown in at least FIGS. 2A-2B. It should also benoted that portions of the method 400 may be carried out by a firstlogical device (e.g., the general purpose processor 122 of FIG. 1 ) andother portions of the method 400 may be carried out by a second logicaldevice (e.g., the Image Signal Processor (ISP) 124 of FIG. 1 ).

In some embodiments, prior to initialization of the method 400, thepixel values of the source image data may be normalized, such that thepixel values of the normalized source image data range from [0, 1]. Themethod 400 begins at block B402, where the mid-tone point is determinedbased on the source image data. In some embodiments, to determine thex-component of the mid-tone point, the source image data may be filteredby a high-tone filter and a low-tone filter, to generate filtered sourceimage data. The high-tone filter filters out the portion of source imagedata that includes pixel values greater than a high-tone thresholdvalue. The low-tone filter filters out the portion of the source imagedata that includes pixel values less than a low-tone threshold value.The x-component of the mid-tone point may be determined by averaging thepixel values of the portion of the source image data that remainssubsequent to the application of the high-tone and low-tone filters. Inother embodiments the high-tone and low-tone filters are not applied tothe source image data.

In some embodiments, the averaging of the pixel values may includelog-averaging the pixel values. In such embodiments, log-transformedimage data pixel values may be generating by applying a logarithmfunction to the filtered or unfiltered source image data. The base ofthe logarithm function may be selected based on the source image data.In one embodiment, the base of the logarithm function is ten. In otherembodiments, the logarithm function may be the natural-logarithmfunction. An average value of the log-transformed pixel values may bedetermined. The average value of the log-transformed pixel values may beexponentiated by the corresponding base of the logarithm function. Thex-component of the mid-tone point may be set to the exponentiation ofthe average value of the log-transformed pixel values of the sourceimage data. The y-component of the mid-tone point may be set to aspecified mid-tone value for the target image data.

At block B404, the low-tone point may be determined based on the sourceimage data. In one non-limiting embodiment, a subset of the pixel valuesof the source image data is determined and/or generated, where eachvalue for the pixels included in the subset is less than the pixelvalues for the pixels that are excluded from the subset. That is, thesource image data may be filtered via a low-tone filter, such that theonly pixel values that remain after the filtering are those pixels withpixel values that are less than a low-tone threshold. The x-component ofthe low-tone point may be determined based on the subset of pixelvalues. For example, the pixel values that survive the low-tonefiltering process may be averaged to determine the x-component of thelow-tone point. The y-component of the low-tone point may be determinedand/or selected to be the smallest pixel value for the target imagedata. In at least one embodiment, the y-component of the low-tone pointis set to 0.0. The low-tone point may be a black point.

At block B406, the high-tone point may be determined based on the sourceimage data. In one non-limiting embodiment, a subset of the pixel valuesof the source image data is determined and/or generated, where eachvalue for the pixels included in the subset is greater than the pixelvalues for the pixels that are excluded from the subset. That is, thesource image data may be filtered via a high-tone filter, such that theonly pixel values that remain after the filtering are pixels with pixelvalues that are greater than a high-tone threshold. The x-component ofthe high-tone point may be determined based on the subset of pixelvalues. For example, the pixel values that survive the high-tonefiltering process may be averaged to determine the x-component of thehigh-tone point. The y-component of the high-tone point may bedetermined and/or selected to be the largest pixel value for the targetimage data. In at least one embodiment, the y-component of the low-tonepoint is set to 1.0. The high-tone may be the white point. By settingthe y-component of the low-tone point to 0.0 and the y-component of thehigh-tone component, the target image data is normalized to the range of[0, 1].

At block B408, a flare suppression point may be determined. Thex-component of the flare-suppression point may be set to a value that isto be the maximal flare that will be suppressed in the tone. In someembodiments, the x-component of the flare-suppression point may be userselected. In other embodiments, the x-component may be dynamicallydetermined based on the pixel values of the source image and/or thedetermined plurality of the statistical metrics for the source imagedata. For example, the x-component of the flare-suppression point may beset based on a percentage of the mid-tone pixel values or the value ofthe pixel values that are low-toned thresholded. In various embodiments,the x-component of the flare-suppression point may be selected to begreater than the x-component of the low-tone point, but less than thex-component of the mid-tone point. In various non-limiting embodiments,the y-component of the flare-suppression point is set to 0.0. In otherembodiments, the y-component of the flare-suppression point may be setor selected to be greater than 0.0, but less than the y-component of themid-tone point.

At optional block B410, one or more additional control points aredetermined based on the source image data. At block B412, the sourcedata may be pre-processed based on the control points. For example, eachof the pixels of the source image data with pixel values that are lessthan the x-component of the low-tone point may be clipped, such that thepixel values of such pixels are set to the scalar value of thex-component of the low-tone point. In at least one embodiment, each ofthe pixels of the source image data with pixel values that are less thanthe x-component of the flare-suppression point may be clipped, such thatthe pixel values of such pixels are set to the scalar value of thex-component of the flare-suppression point. Furthermore, each of thepixels of the source image data with pixel values that are greater thanthe x-component of the high-tone point may be clipped, such that thepixel values of such pixels are set to the scalar value of thex-component of the high-tone point.

At block B414, a gain value may be determined based on the mid-tonepoint and the flare-suppression point. For example, a gain line may beconstructed through the mid-tone point and the flare-suppression point.The gain value may be set to be the slope of the gain value line. Invarious embodiments the slope is positive. An embodiment of a gain valueline, and corresponding slope, are shown in FIG. 2A.

At block B416, the tone mapping function is determined based on thelow-tone point, the mid-tone point, and the high-tone point. In someembodiments, the determination of the tone mapping function is furtherbased on the gain value. In still other embodiments, the determinationof the tone mapping function is further based on the one or moreadditional tone control points determined in block B410. In variousembodiments, the tone map generator 156 of FIG. 1 may be employed todetermine the tone mapping function. More specifically, the tone mapgenerator 156 may employ the general purpose processor 122 of FIG. 1 togenerate the tone mapping function.

To determine the tone mapping functions, one or more parametricfunctions may be fit, wherein the fit is constrained by at least aportion of the various tone control points. The parametric functions mayinclude one or more polynomials, of virtually any degree. In someembodiments, the fitting of the tone mapping function may beconstrained, such that the tone mapping function includes and/orintersects at least the low-tone point, the mid-tone point, and thehigh-tone point. In still further embodiments, the fitting of the tonemapping function may be constrained, such that the tone mapping functionincludes and/or intersects the additional tone control points. In someembodiments, the fitting of the tone mapping function is constrained,such that the derivative and/or the instantaneous rate of change of thetone mapping function, evaluated at the x-component of the mid-tonepoints is equivalent to the gain value. An embodiment of a tone mappingfunction is shown in FIG. 2B.

Various spline methods may be employed to fit and/or generate the tonemapping function. Generating the tone mapping function may includegenerating and/or constructing a non-linear curve. The non-linear curvemay be a global tone curve (GTC). The curve may include a plurality oflinear or curved segments (e.g., a plurality of splines). The curve maybe a Bezier curve, e.g., a quadratic or a cubic Bezier curve. The curvemay be constructed via second-, third, or higher order parametricequations. Various spline methods may be employed to generate the curve.The joint between the splines or segments could be constructed to ensurethat the derivative of the tone mapping function is continuous.

At block B418, the tone mapping function may be applied to generate thetarget image data. In various embodiments, the tone map applicator 158of FIG. 1 may be employed to transform the source image data into targetimage data, via the tone mapping function. Tone map applicator 158 mayemploy the ISP 124 of FIG. 1 to apply the non-linear transformation ofto the source image data. In at least one embodiment, a pipeline of theISP 124 may be employed to apply the transformation and generate thetarget image data.

Blocks B420 and B422 are optional blocks that may be employed togenerate SDR target image data or LDR image date from the tone mappedtarget image data. At block B420, a gamma-compression function may beapplied to the tone mapped image data to generate color-compressed imagedata. At block B422, either SDR or LDR target image data may begenerated based on the color compresses source image data.

FIG. 5 is a flow diagram showing the method 500 for distributing theoperations of tone mapping, in accordance with some embodiments of thepresent disclosure. At block B502, a first logic device may be employedto determine the tone mapping function. At block B502, the first logicdevice may be any of the logic devices 120 of FIG. 1 , such as but notlimited to the general purpose processor 122, the image signal processor(ISP) 124, the ASIC 126, and/or the FPGA 128. In some embodiments, thefirst logic device that is employed to determine the tone mappingfunction is the general purpose processor 122. In at least oneembodiment, a Graphics Processing Unit (GPU) is employed to determinethe tone mapping function.

At block B504, a second logic device may be employed to apply the tonemapping function to the source image data and generate the target imagesource data. At block B504, the second logic device may be any of thelogic devices 120, such as but not limited to the general purposeprocessor 122, the ISP 124, the ASIC 126, and/or the FPGA 128. In someembodiments, the second logic device that is employed to apply the tonemapping function is the ISP 124. A pipeline of the ISP 124 may beemployed to apply the tone mapping function and transform the sourceimage data to the target image data. In at least one embodiment, a GPUis employed to determine the tone mapping function.

Example Embodiments of an Autonomous Vehicle

FIG. 6A is an illustration of an example autonomous vehicle 104, inaccordance with some embodiments of the present disclosure. Theautonomous vehicle 104 (alternatively referred to herein as the “vehicle104”) may include a passenger vehicle, such as a car, a truck, a bus,and/or another type of vehicle that accommodates one or more passengers.Autonomous vehicles are generally described in terms of automationlevels, defined by the National Highway Traffic Safety Administration(NHTSA), a division of the US Department of Transportation, and theSociety of Automotive Engineers (SAE) “Taxonomy and Definitions forTerms Related to Driving Automation Systems for On-Road Motor Vehicles”(Standard No. J3016-201806, published on Jun. 15, 2018, Standard No.J3016-201609, published on Sep. 30, 2016, and previous and futureversions of this standard). The vehicle 104 may be capable offunctionality in accordance with one or more of Level 3-Level 5 of theautonomous driving levels. For example, the vehicle 104 may be capableof conditional automation (Level 3), high automation (Level 4), and/orfull automation (Level 5), depending on the embodiment.

The vehicle 104 may include components such as a chassis, a vehiclebody, wheels (e.g., 2, 4, 6, 8, 18, etc.), tires, axles, and othercomponents of a vehicle. The vehicle 104 may include a propulsion system650, such as an internal combustion engine, hybrid electric power plant,an all-electric engine, and/or another propulsion system type. Thepropulsion system 650 may be connected to a drive train of the vehicle104, which may include a transmission, to enable the propulsion of thevehicle 104. The propulsion system 650 may be controlled in response toreceiving signals from the throttle/accelerator 652.

A steering system 654, which may include a steering wheel, may be usedto steer the vehicle 104 (e.g., along a desired path or route) when thepropulsion system 650 is operating (e.g., when the vehicle is inmotion). The steering system 654 may receive signals from a steeringactuator 656. The steering wheel may be optional for full automation(Level 5) functionality.

The brake sensor system 646 may be used to operate the vehicle brakes inresponse to receiving signals from the brake actuators 648 and/or brakesensors.

Controller(s) 636, which may include one or more system on chips (SoCs)604 (FIG. 6C) and/or GPU(s), may provide signals (e.g., representativeof commands) to one or more components and/or systems of the vehicle104. For example, the controller(s) may send signals to operate thevehicle brakes via one or more brake actuators 648, to operate thesteering system 654 via one or more steering actuators 656, to operatethe propulsion system 650 via one or more throttle/accelerators 652. Thecontroller(s) 636 may include one or more onboard (e.g., integrated)computing devices (e.g., supercomputers) that process sensor signals,and output operation commands (e.g., signals representing commands) toenable autonomous driving and/or to assist a human driver in driving thevehicle 104. The controller(s) 636 may include a first controller 636for autonomous driving functions, a second controller 636 for functionalsafety functions, a third controller 636 for artificial intelligencefunctionality (e.g., computer vision), a fourth controller 636 forinfotainment functionality, a fifth controller 636 for redundancy inemergency conditions, and/or other controllers. In some examples, asingle controller 636 may handle two or more of the abovefunctionalities, two or more controllers 636 may handle a singlefunctionality, and/or any combination thereof.

The controller(s) 636 may provide the signals for controlling one ormore components and/or systems of the vehicle 104 in response to sensordata received from one or more sensors (e.g., sensor inputs). The sensordata may be received from, for example and without limitation, globalnavigation satellite systems sensor(s) 658 (e.g., Global PositioningSystem sensor(s)), RADAR sensor(s) 660, ultrasonic sensor(s) 662, LIDARsensor(s) 664, inertial measurement unit (IMU) sensor(s) 666 (e.g.,accelerometer(s), gyroscope(s), magnetic compass(es), magnetometer(s),etc.), microphone(s) 696, stereo camera(s) 668, wide-view camera(s) 670(e.g., fisheye cameras), infrared camera(s) 672, surround camera(s) 674(e.g., 360 degree cameras), long-range and/or mid-range camera(s) 698,speed sensor(s) 644 (e.g., for measuring the speed of the vehicle 104),vibration sensor(s) 642, steering sensor(s) 640, brake sensor(s) (e.g.,as part of the brake sensor system 646), and/or other sensor types.

One or more of the controller(s) 636 may receive inputs (e.g.,represented by input data) from an instrument cluster 632 of the vehicle104 and provide outputs (e.g., represented by output data, display data,etc.) via a human-machine interface (HMI) display 634, an audibleannunciator, a loudspeaker, and/or via other components of the vehicle104. The outputs may include information such as vehicle velocity,speed, time, map data (e.g., the HD map 622 of FIG. 6C), location data(e.g., the vehicle's 104 location, such as on a map), direction,location of other vehicles (e.g., an occupancy grid), information aboutobjects and status of objects as perceived by the controller(s) 636,etc. For example, the HMI display 634 may display information about thepresence of one or more objects (e.g., a street sign, caution sign,traffic light changing, etc.), and/or information about drivingmaneuvers the vehicle has made, is making, or will make (e.g., changinglanes now, taking exit 34B in two miles, etc.).

The vehicle 104 further includes a network interface 624 which may useone or more wireless antenna(s) 626 and/or modem(s) to communicate overone or more networks. For example, the network interface 624 may becapable of communication over LTE, WCDMA, UMTS, GSM, CDMA2000, etc. Thewireless antenna(s) 626 may also enable communication between objects inthe environment (e.g., vehicles, mobile devices, etc.), using local areanetwork(s), such as Bluetooth, Bluetooth LE, Z-Wave, ZigBee, etc.,and/or low power wide-area network(s) (LPWANs), such as LoRaWAN, SigFox,etc.

FIG. 6B is an example of camera locations and fields of view for theexample autonomous vehicle 104 of FIG. 6A, in accordance with someembodiments of the present disclosure. The cameras and respective fieldsof view are one example embodiment and are not intended to be limiting.For example, additional and/or alternative cameras may be includedand/or the cameras may be located at different locations on the vehicle104.

The camera types for the cameras may include, but are not limited to,digital cameras that may be adapted for use with the components and/orsystems of the vehicle 104. The camera(s) may operate at automotivesafety integrity level (ASIL) B and/or at another ASIL. The camera typesmay be capable of any image capture rate, such as 60 frames per second(fps), 620 fps, 240 fps, etc., depending on the embodiment. The camerasmay be capable of using rolling shutters, global shutters, another typeof shutter, or a combination thereof. In some examples, the color filterarray may include a red clear clear clear (RCCC) color filter array, ared clear clear blue (RCCB) color filter array, a red blue green clear(RBGC) color filter array, a Foveon X3 color filter array, a Bayersensors (RGGB) color filter array, a monochrome sensor color filterarray, and/or another type of color filter array. In some embodiments,clear pixel cameras, such as cameras with an RCCC, an RCCB, and/or anRBGC color filter array, may be used in an effort to increase lightsensitivity.

In some examples, one or more of the camera(s) may be used to performadvanced driver assistance systems (ADAS) functions (e.g., as part of aredundant or fail-safe design). For example, a Multi-Function MonoCamera may be installed to provide functions including lane departurewarning, traffic sign assist and intelligent headlamp control. One ormore of the camera(s) (e.g., all of the cameras) may record and provideimage data (e.g., video) simultaneously.

One or more of the cameras may be mounted in a mounting assembly, suchas a custom designed (3-D printed) assembly, in order to cut out straylight and reflections from within the car (e.g., reflections from thedashboard reflected in the windshield mirrors) which may interfere withthe camera's image data capture abilities. With reference to wing-mirrormounting assemblies, the wing-mirror assemblies may be custom 3-Dprinted so that the camera mounting plate matches the shape of thewing-mirror. In some examples, the camera(s) may be integrated into thewing-mirror. For side-view cameras, the camera(s) may also be integratedwithin the four pillars at each corner of the cabin.

Cameras with a field of view that include portions of the environment infront of the vehicle 104 (e.g., front-facing cameras) may be used forsurround view, to help identify forward facing paths and obstacles, aswell aid in, with the help of one or more controllers 636 and/or controlSoCs, providing information critical to generating an occupancy gridand/or determining the preferred vehicle paths. Front-facing cameras maybe used to perform many of the same ADAS functions as LIDAR, includingemergency braking, pedestrian detection, and collision avoidance.Front-facing cameras may also be used for ADAS functions and systemsincluding Lane Departure Warnings (“LDW”), Autonomous Cruise Control(“ACC”), and/or other functions such as traffic sign recognition.

A variety of cameras may be used in a front-facing configuration,including, for example, a monocular camera platform that includes a CMOS(complementary metal oxide semiconductor) color imager. Another examplemay be a wide-view camera(s) 670 that may be used to perceive objectscoming into view from the periphery (e.g., pedestrians, crossing trafficor bicycles). Although only one wide-view camera is illustrated in FIG.6B, there may any number of wide-view cameras 670 on the vehicle 104. Inaddition, long-range camera(s) 698 (e.g., along-view stereo camera pair)may be used for depth-based object detection, especially for objects forwhich a neural network has not yet been trained. The long-rangecamera(s) 698 may also be used for object detection and classification,as well as basic object tracking.

One or more stereo cameras 668 may also be included in a front-facingconfiguration. The stereo camera(s) 668 may include an integratedcontrol unit comprising a scalable processing unit, which may provide aprogrammable logic (FPGA) and a multi-core micro-processor with anintegrated CAN or Ethernet interface on a single chip. Such a unit maybe used to generate a 3-D map of the vehicle's environment, including adistance estimate for all the points in the image. An alternative stereocamera(s) 668 may include a compact stereo vision sensor(s) that mayinclude two camera lenses (one each on the left and right) and an imageprocessing chip that may measure the distance from the vehicle to thetarget object and use the generated information (e.g., metadata) toactivate the autonomous emergency braking and lane departure warningfunctions. Other types of stereo camera(s) 668 may be used in additionto, or alternatively from, those described herein.

Cameras with a field of view that include portions of the environment tothe side of the vehicle 104 (e.g., side-view cameras) may be used forsurround view, providing information used to create and update theoccupancy grid, as well as to generate side impact collision warnings.For example, surround camera(s) 674 (e.g., four surround cameras 674 asillustrated in FIG. 6B) may be positioned to on the vehicle 104. Thesurround camera(s) 674 may include wide-view camera(s) 670, fisheyecamera(s), 360 degree camera(s), and/or the like. Four example, fourfisheye cameras may be positioned on the vehicle's front, rear, andsides. In an alternative arrangement, the vehicle may use three surroundcamera(s) 674 (e.g., left, right, and rear), and may leverage one ormore other camera(s) (e.g., a forward-facing camera) as a fourthsurround view camera.

Cameras with a field of view that include portions of the environment tothe rear of the vehicle 104 (e.g., rear-view cameras) may be used forpark assistance, surround view, rear collision warnings, and creatingand updating the occupancy grid. A wide variety of cameras may be usedincluding, but not limited to, cameras that are also suitable as afront-facing camera(s) (e.g., long-range and/or mid-range camera(s) 698,stereo camera(s) 668), infrared camera(s) 672, etc.), as describedherein.

FIG. 6C is a block diagram of an example system architecture for theexample autonomous vehicle 104 of FIG. 6A, in accordance with someembodiments of the present disclosure. It should be understood that thisand other arrangements described herein are set forth only as examples.Other arrangements and elements (e.g., machines, interfaces, functions,orders, groupings of functions, etc.) may be used in addition to orinstead of those shown, and some elements may be omitted altogether.Further, many of the elements described herein are functional entitiesthat may be implemented as discrete or distributed components or inconjunction with other components, and in any suitable combination andlocation. Various functions described herein as being performed byentities may be carried out by hardware, firmware, and/or software. Forinstance, various functions may be carried out by a processor executinginstructions stored in memory.

Each of the components, features, and systems of the vehicle 104 in FIG.6C are illustrated as being connected via bus 602. The bus 602 mayinclude a Controller Area Network (CAN) data interface (alternativelyreferred to herein as a “CAN bus”). A CAN may be a network inside thevehicle 104 used to aid in control of various features and functionalityof the vehicle 104, such as actuation of brakes, acceleration, braking,steering, windshield wipers, etc. A CAN bus may be configured to havedozens or even hundreds of nodes, each with its own unique identifier(e.g., a CAN ID). The CAN bus may be read to find steering wheel angle,ground speed, engine revolutions per minute (RPMs), button positions,and/or other vehicle status indicators. The CAN bus may be ASIL Bcompliant.

Although the bus 602 is described herein as being a CAN bus, this is notintended to be limiting. For example, in addition to, or alternativelyfrom, the CAN bus, FlexRay and/or Ethernet may be used. Additionally,although a single line is used to represent the bus 602, this is notintended to be limiting. For example, there may be any number of busses602, which may include one or more CAN busses, one or more FlexRaybusses, one or more Ethernet busses, and/or one or more other types ofbusses using a different protocol. In some examples, two or more busses602 may be used to perform different functions, and/or may be used forredundancy. For example, a first bus 602 may be used for collisionavoidance functionality and a second bus 602 may be used for actuationcontrol. In any example, each bus 602 may communicate with any of thecomponents of the vehicle 104, and two or more busses 602 maycommunicate with the same components. In some examples, each SoC 604,each controller 636, and/or each computer within the vehicle may haveaccess to the same input data (e.g., inputs from sensors of the vehicle104), and may be connected to a common bus, such the CAN bus.

The vehicle 104 may include one or more controller(s) 636, such as thosedescribed herein with respect to FIG. 6A. The controller(s) 636 may beused for a variety of functions. The controller(s) 636 may be coupled toany of the various other components and systems of the vehicle 104, andmay be used for control of the vehicle 104, artificial intelligence ofthe vehicle 104, infotainment for the vehicle 104, and/or the like.

The vehicle 104 may include a system(s) on a chip (SoC) 604. The SoC 604may include CPU(s) 606, GPU(s) 608, processor(s) 610, cache(s) 612,accelerator(s) 614, data store(s) 616, and/or other components andfeatures not illustrated. The SoC(s) 604 may be used to control thevehicle 104 in a variety of platforms and systems. For example, theSoC(s) 604 may be combined in a system (e.g., the system of the vehicle104) with an HD map 622 which may obtain map refreshes and/or updatesvia a network interface 624 from one or more servers (e.g., server(s)678 of FIG. 6D).

The CPU(s) 606 may include a CPU cluster or CPU complex (alternativelyreferred to herein as a “CCPLEX”). The CPU(s) 606 may include multiplecores and/or L2 caches. For example, in some embodiments, the CPU(s) 606may include eight cores in a coherent multi-processor configuration. Insome embodiments, the CPU(s) 606 may include four dual-core clusterswhere each cluster has a dedicated L2 cache (e.g., a 2 MB L2 cache). TheCPU(s) 606 (e.g., the CCPLEX) may be configured to support simultaneouscluster operation enabling any combination of the clusters of the CPU(s)606 to be active at any given time.

The CPU(s) 606 may implement power management capabilities that includeone or more of the following features: individual hardware blocks may beclock-gated automatically when idle to save dynamic power; each coreclock may be gated when the core is not actively executing instructionsdue to execution of WFI/WFE instructions; each core may be independentlypower-gated; each core cluster may be independently clock-gated when allcores are clock-gated or power-gated; and/or each core cluster may beindependently power-gated when all cores are power-gated. The CPU(s) 606may further implement an enhanced algorithm for managing power states,where allowed power states and expected wakeup times are specified, andthe hardware/microcode determines the best power state to enter for thecore, cluster, and CCPLEX. The processing cores may support simplifiedpower state entry sequences in software with the work offloaded tomicrocode.

The GPU(s) 608 may include an integrated GPU (alternatively referred toherein as an “iGPU”). The GPU(s) 608 may be programmable and may beefficient for parallel workloads. The GPU(s) 608, in some examples, mayuse an enhanced tensor instruction set. The GPU(s) 608 may include oneor more streaming microprocessors, where each streaming microprocessormay include an L1 cache (e.g., an L1 cache with at least 96 KB storagecapacity), and two or more of the streaming microprocessors may share anL2 cache (e.g., an L2 cache with a 512 KB storage capacity). In someembodiments, the GPU(s) 608 may include at least eight streamingmicroprocessors. The GPU(s) 608 may use compute application programminginterface(s) (API(s)). In addition, the GPU(s) 608 may use one or moreparallel computing platforms and/or programming models (e.g., NVIDIA'sCUDA).

The GPU(s) 608 may be power-optimized for best performance in automotiveand embedded use cases. For example, the GPU(s) 608 may be fabricated ona Fin field-effect transistor (FinFET). However, this is not intended tobe limiting and the GPU(s) 608 may be fabricated using othersemiconductor manufacturing processes. Each streaming microprocessor mayincorporate a number of mixed-precision processing cores partitionedinto multiple blocks. For example, and without limitation, 64 PF32 coresand 32 PF64 cores may be partitioned into four processing blocks. Insuch an example, each processing block may be allocated 16 FP32 cores, 8FP64 cores, 16 INT32 cores, two mixed-precision NVIDIA TENSOR COREs fordeep learning matrix arithmetic, an L0 instruction cache, a warpscheduler, a dispatch unit, and/or a 64 KB register file. In addition,the streaming microprocessors may include independent parallel integerand floating-point data paths to provide for efficient execution ofworkloads with a mix of computation and addressing calculations. Thestreaming microprocessors may include independent thread schedulingcapability to enable finer-grain synchronization and cooperation betweenparallel threads. The streaming microprocessors may include a combinedL1 data cache and shared memory unit in order to improve performancewhile simplifying programming.

The GPU(s) 608 may include a high bandwidth memory (HBM) and/or a 16 GBHBM2 memory subsystem to provide, in some examples, about 900 GB/secondpeak memory bandwidth. In some examples, in addition to, oralternatively from, the HBM memory, a synchronous graphics random-accessmemory (SGRAM) may be used, such as a graphics double data rate typefive synchronous random-access memory (GDDR5).

The GPU(s) 608 may include unified memory technology including accesscounters to allow for more accurate migration of memory pages to theprocessor that accesses them most frequently, thereby improvingefficiency for memory ranges shared between processors. In someexamples, address translation services (ATS) support may be used toallow the GPU(s) 608 to access the CPU(s) 606 page tables directly. Insuch examples, when the GPU(s) 608 memory management unit (MMU)experiences a miss, an address translation request may be transmitted tothe CPU(s) 606. In response, the CPU(s) 606 may look in its page tablesfor the virtual-to-physical mapping for the address and transmits thetranslation back to the GPU(s) 608. As such, unified memory technologymay allow a single unified virtual address space for memory of both theCPU(s) 606 and the GPU(s) 608, thereby simplifying the GPU(s) 608programming and porting of applications to the GPU(s) 608.

In addition, the GPU(s) 608 may include an access counter that may keeptrack of the frequency of access of the GPU(s) 608 to memory of otherprocessors. The access counter may help ensure that memory pages aremoved to the physical memory of the processor that is accessing thepages most frequently.

The SoC(s) 604 may include any number of cache(s) 612, including thosedescribed herein. For example, the cache(s) 612 may include an L3 cachethat is available to both the CPU(s) 606 and the GPU(s) 608 (e.g., thatis connected both the CPU(s) 606 and the GPU(s) 608). The cache(s) 612may include a write-back cache that may keep track of states of lines,such as by using a cache coherence protocol (e.g., MEI, MESI, MSI,etc.). The L3 cache may include 4 MB or more, depending on theembodiment, although smaller cache sizes may be used.

The SoC(s) 604 may include one or more accelerators 614 (e.g., hardwareaccelerators, software accelerators, or a combination thereof). Forexample, the SoC(s) 604 may include a hardware acceleration cluster thatmay include optimized hardware accelerators and/or large on-chip memory.The large on-chip memory (e.g., 4 MB of SRAM), may enable the hardwareacceleration cluster to accelerate neural networks and othercalculations. The hardware acceleration cluster may be used tocomplement the GPU(s) 608 and to off-load some of the tasks of theGPU(s) 608 (e.g., to free up more cycles of the GPU(s) 608 forperforming other tasks). As an example, the accelerator(s) 614 may beused for targeted workloads (e.g., perception, convolutional neuralnetworks (CNNs), etc.) that are stable enough to be amenable toacceleration. The term “CNN,” as used herein, may include all types ofCNNs, including region-based or regional convolutional neural networks(RCNNs) and Fast RCNNs (e.g., as used for object detection).

The accelerator(s) 614 (e.g., the hardware acceleration cluster) mayinclude a deep learning accelerator(s) (DLA). The DLA(s) may include oneor more Tensor processing units (TPUs) that may be configured to providean additional ten trillion operations per second for deep learningapplications and inferencing. The TPUs may be accelerators configuredto, and optimized for, performing image processing functions (e.g., forCNNs, RCNNs, etc.). The DLA(s) may further be optimized for a specificset of neural network types and floating point operations, as well asinferencing. The design of the DLA(s) may provide more performance permillimeter than a general-purpose GPU, and vastly exceeds theperformance of a CPU. The TPU(s) may perform several functions,including a single-instance convolution function, supporting, forexample, INT8, INT16, and FP16 data types for both features and weights,as well as post-processor functions.

The DLA(s) may quickly and efficiently execute neural networks,especially CNNs, on processed or unprocessed data for any of a varietyof functions, including, for example and without limitation: a CNN forobject identification and detection using data from camera sensors; aCNN for distance estimation using data from camera sensors; a CNN foremergency vehicle detection and identification and detection using datafrom microphones; a CNN for facial recognition and vehicle owneridentification using data from camera sensors; and/or a CNN for securityand/or safety related events.

The DLA(s) may perform any function of the GPU(s) 608, and by using aninference accelerator, for example, a designer may target either theDLA(s) or the GPU(s) 608 for any function. For example, the designer mayfocus processing of CNNs and floating point operations on the DLA(s) andleave other functions to the GPU(s) 608 and/or other accelerator(s) 614.

The accelerator(s) 614 (e.g., the hardware acceleration cluster) mayinclude a programmable vision accelerator(s) (PVA), which mayalternatively be referred to herein as a computer vision accelerator.The PVA(s) may be designed and configured to accelerate computer visionalgorithms for the advanced driver assistance systems (ADAS), autonomousdriving, and/or augmented reality (AR) and/or virtual reality (VR)applications. The PVA(s) may provide a balance between performance andflexibility. For example, each PVA(s) may include, for example andwithout limitation, any number of reduced instruction set computer(RISC) cores, direct memory access (DMA), and/or any number of vectorprocessors.

The RISC cores may interact with image sensors (e.g., the image sensorsof any of the cameras described herein), image signal processor(s),and/or the like. Each of the RISC cores may include any amount ofmemory. The RISC cores may use any of a number of protocols, dependingon the embodiment. In some examples, the RISC cores may execute areal-time operating system (RTOS). The RISC cores may be implementedusing one or more integrated circuit devices, application specificintegrated circuits (ASICs), and/or memory devices. For example, theRISC cores may include an instruction cache and/or a tightly coupledRAM.

The DMA may enable components of the PVA(s) to access the system memoryindependently of the CPU(s) 606. The DMA may support any number offeatures used to provide optimization to the PVA including, but notlimited to, supporting multi-dimensional addressing and/or circularaddressing. In some examples, the DMA may support up to six or moredimensions of addressing, which may include block width, block height,block depth, horizontal block stepping, vertical block stepping, and/ordepth stepping.

The vector processors may be programmable processors that may bedesigned to efficiently and flexibly execute programming for computervision algorithms and provide signal processing capabilities. In someexamples, the PVA may include a PVA core and two vector processingsubsystem partitions. The PVA core may include a processor subsystem,DMA engine(s) (e.g., two DMA engines), and/or other peripherals. Thevector processing subsystem may operate as the primary processing engineof the PVA, and may include a vector processing unit (VPU), aninstruction cache, and/or vector memory (e.g., VMEM). A VPU core mayinclude a digital signal processor such as, for example, a singleinstruction, multiple data (SIMD), very long instruction word (VLIW)digital signal processor. The combination of the SIMD and VLIW mayenhance throughput and speed.

Each of the vector processors may include an instruction cache and maybe coupled to dedicated memory. As a result, in some examples, each ofthe vector processors may be configured to execute independently of theother vector processors. In other examples, the vector processors thatare included in a particular PVA may be configured to employ dataparallelism. For example, in some embodiments, the plurality of vectorprocessors included in a single PVA may execute the same computer visionalgorithm, but on different regions of an image. In other examples, thevector processors included in a particular PVA may simultaneouslyexecute different computer vision algorithms, on the same image, or evenexecute different algorithms on sequential images or portions of animage. Among other things, any number of PVAs may be included in thehardware acceleration cluster and any number of vector processors may beincluded in each of the PVAs. In addition, the PVA(s) may includeadditional error correcting code (ECC) memory, to enhance overall systemsafety.

The accelerator(s) 614 (e.g., the hardware acceleration cluster) mayinclude a computer vision network on-chip and SRAM, for providing ahigh-bandwidth, low latency SRAM for the accelerator(s) 614. In someexamples, the on-chip memory may include at least 4 MB SRAM, consistingof, for example and without limitation, eight field-configurable memoryblocks, that may be accessible by both the PVA and the DLA. Each pair ofmemory blocks may include an advanced peripheral bus (APB) interface,configuration circuitry, a controller, and a multiplexer. Any type ofmemory may be used. The PVA and DLA may access the memory via a backbonethat provides the PVA and DLA with high-speed access to memory. Thebackbone may include a computer vision network on-chip thatinterconnects the PVA and the DLA to the memory (e.g., using the APB).

The computer vision network on-chip may include an interface thatdetermines, before transmission of any control signal/address/data, thatboth the PVA and the DLA provide ready and valid signals. Such aninterface may provide for separate phases and separate channels fortransmitting control signals/addresses/data, as well as burst-typecommunications for continuous data transfer. This type of interface maycomply with ISO 26262 or IEC 61508 standards, although other standardsand protocols may be used.

In some examples, the SoC(s) 604 may include a real-time ray-tracinghardware accelerator, such as described in U.S. patent application Ser.No. 16/101,232, filed on Aug. 10, 2018. The real-time ray-tracinghardware accelerator may be used to quickly and efficiently determinethe positions and extents of objects (e.g., within a world model), togenerate real0time visualization simulations, for RADAR signalinterpretation, for sound propagation synthesis and/or analysis, forsimulation of SONAR systems, for general wave propagation simulation,for comparison to LIDAR data for purposes of localization and/or otherfunctions, and/or for other uses.

The accelerator(s) 614 (e.g., the hardware accelerator cluster) have awide array of uses for autonomous driving. The PVA may be a programmablevision accelerator that may be used for key processing stages in ADASand autonomous vehicles. The PVA's capabilities are a good match foralgorithmic domains needing predictable processing, at low power and lowlatency. In other words, the PVA performs well on semi-dense or denseregular computation, even on small data sets, which need predictablerun-times with low latency and low power. Thus, in the context ofplatforms for autonomous vehicles, the PVAs are designed to run classiccomputer vision algorithms, as they are efficient at object detectionand operating on integer math.

For example, according to one embodiment of the technology, the PVA isused to perform computer stereo vision. A semi-global matching-basedalgorithm may be used in some examples, although this is not intended tobe limiting. Many applications for Level 3-5 autonomous driving requiremotion estimation/stereo matching on-the-fly (e.g., structure frommotion, pedestrian recognition, lane detection, etc.). The PVA mayperform computer stereo vision function on inputs from two monocularcameras.

In some examples, the PVA may be used to perform dense optical flow.According to process raw RADAR data (e.g., using a 4D Fast FourierTransform) to provide Processed RADAR. In other examples, the PVA isused for time of flight depth processing, by processing raw time offlight data to provide processed time of flight data, for example.

The DLA may be used to run any type of network to enhance control anddriving safety, including for example, a neural network that outputs ameasure of confidence for each object detection. Such a confidence valuemay be interpreted as a probability, or as providing a relative “weight”of each detection compared to other detections. This confidence valueenables the system to make further decisions regarding which detectionsshould be considered as true positive detections rather than falsepositive detections. For example, the system may set a threshold valuefor the confidence and consider only the detections exceeding thethreshold value as true positive detections. In an automatic emergencybraking (AEB) system, false positive detections would cause the vehicleto automatically perform emergency braking, which is obviouslyundesirable. Therefore, only the most confident detections should beconsidered as triggers for AEB. The DLA may run a neural network forregressing the confidence value. The neural network may take as itsinput at least some subset of parameters, such as bounding boxdimensions, ground plane estimate obtained (e.g. from anothersubsystem), inertial measurement unit (IMU) sensor 666 output thatcorrelates with the vehicle 104 orientation, distance, 3D locationestimates of the object obtained from the neural network and/or othersensors (e.g., LIDAR sensor(s) 664 or RADAR sensor(s) 660), amongothers.

The SoC(s) 604 may include data store(s) 616 (e.g., memory). The datastore(s) 616 may be on-chip memory of the SoC(s) 604, which may storeneural networks to be executed on the GPU and/or the DLA. In someexamples, the data store(s) 616 may be large enough in capacity to storemultiple instances of neural networks for redundancy and safety. Thedata store(s) 612 may comprise L2 or L3 cache(s) 612. Reference to thedata store(s) 616 may include reference to the memory associated withthe PVA, DLA, and/or other accelerator(s) 614, as described herein.

The SoC(s) 604 may include one or more processor(s) 610 (e.g., embeddedprocessors). The processor(s) 610 may include a boot and powermanagement processor that may be a dedicated processor and subsystem tohandle boot power and management functions and related securityenforcement. The boot and power management processor may be a part ofthe SoC(s) 604 boot sequence and may provide runtime power managementservices. The boot power and management processor may provide clock andvoltage programming, assistance in system low power state transitions,management of SoC(s) 604 thermals and temperature sensors, and/ormanagement of the SoC(s) 604 power states. Each temperature sensor maybe implemented as a ring-oscillator whose output frequency isproportional to temperature, and the SoC(s) 604 may use thering-oscillators to detect temperatures of the CPU(s) 606, GPU(s) 608,and/or accelerator(s) 614. If temperatures are determined to exceed athreshold, the boot and power management processor may enter atemperature fault routine and put the SoC(s) 604 into a lower powerstate and/or put the vehicle 104 into a chauffeur to safe stop mode(e.g., bring the vehicle 104 to a safe stop).

The processor(s) 610 may further include a set of embedded processorsthat may serve as an audio processing engine. The audio processingengine may be an audio subsystem that enables full hardware support formulti-channel audio over multiple interfaces, and a broad and flexiblerange of audio I/O interfaces. In some examples, the audio processingengine is a dedicated processor core with a digital signal processorwith dedicated RAM.

The processor(s) 610 may further include an always on processor enginethat may provide necessary hardware features to support low power sensormanagement and wake use cases. The always on processor engine mayinclude a processor core, a tightly coupled RAM, supporting peripherals(e.g., timers and interrupt controllers), various I/O controllerperipherals, and routing logic.

The processor(s) 610 may further include a safety cluster engine thatincludes a dedicated processor subsystem to handle safety management forautomotive applications. The safety cluster engine may include two ormore processor cores, a tightly coupled RAM, support peripherals (e.g.,timers, an interrupt controller, etc.), and/or routing logic. In asafety mode, the two or more cores may operate in a lockstep mode andfunction as a single core with comparison logic to detect anydifferences between their operations.

The processor(s) 610 may further include a real-time camera engine thatmay include a dedicated processor subsystem for handling real-timecamera management.

The processor(s) 610 may further include a high-dynamic range signalprocessor that may include an image signal processor that is a hardwareengine that is part of the camera processing pipeline.

The processor(s) 610 may include a video image compositor that may be aprocessing block (e.g., implemented on a microprocessor) that implementsvideo post-processing functions needed by a video playback applicationto produce the final image for the player window. The video imagecompositor may perform lens distortion correction on wide-view camera(s)670, surround camera(s) 674, and/or on in-cabin monitoring camerasensors. In-cabin monitoring camera sensor is preferably monitored by aneural network running on another instance of the Advanced SoC,configured to identify in cabin events and respond accordingly. Anin-cabin system may perform lip reading to activate cellular service andplace a phone call, dictate emails, change the vehicle's destination,activate or change the vehicle's infotainment system and settings, orprovide voice-activated web surfing. Certain functions are available tothe driver only when the vehicle is operating in an autonomous mode, andare disabled otherwise.

The video image compositor may include enhanced temporal noise reductionfor both spatial and temporal noise reduction. For example, where motionoccurs in a video, the noise reduction weights spatial informationappropriately, decreasing the weight of information provided by adjacentframes. Where an image or portion of an image does not include motion,the temporal noise reduction performed by the video image compositor mayuse information from the previous image to reduce noise in the currentimage.

The video image compositor may also be configured to perform stereorectification on input stereo lens frames. The video image compositormay further be used for user interface composition when the operatingsystem desktop is in use, and the GPU(s) 608 is not required tocontinuously render new surfaces. Even when the GPU(s) 608 is powered onand active doing 3D rendering, the video image compositor may be used tooffload the GPU(s) 608 to improve performance and responsiveness.

The SoC(s) 604 may further include a mobile industry processor interface(MIPI) camera serial interface for receiving video and input fromcameras, a high-speed interface, and/or a video input block that may beused for camera and related pixel input functions. The SoC(s) 604 mayfurther include an input/output controller(s) that may be controlled bysoftware and may be used for receiving I/O signals that are uncommittedto a specific role.

The SoC(s) 604 may further include a broad range of peripheralinterfaces to enable communication with peripherals, audio codecs, powermanagement, and/or other devices. The SoC(s) 604 may be used to processdata from cameras (e.g., connected over Gigabit Multimedia Serial Linkand Ethernet), sensors (e.g., LIDAR sensor(s) 664, RADAR sensor(s) 660,etc. that may be connected over Ethernet), data from bus 602 (e.g.,speed of vehicle 104, steering wheel position, etc.), data from GNSSsensor(s) 658 (e.g., connected over Ethernet or CAN bus). The SoC(s) 604may further include dedicated high-performance mass storage controllersthat may include their own DMA engines, and that may be used to free theCPU(s) 606 from routine data management tasks.

The SoC(s) 604 may be an end-to-end platform with a flexiblearchitecture that spans automation levels 3-5, thereby providing acomprehensive functional safety architecture that leverages and makesefficient use of computer vision and ADAS techniques for diversity andredundancy, provides a platform for a flexible, reliable drivingsoftware stack, along with deep learning tools. The SoC(s) 604 may befaster, more reliable, and even more energy-efficient andspace-efficient than conventional systems. For example, theaccelerator(s) 614, when combined with the CPU(s) 606, the GPU(s) 608,and the data store(s) 616, may provide for a fast, efficient platformfor level 3-5 autonomous vehicles.

The technology thus provides capabilities and functionality that cannotbe achieved by conventional systems. For example, computer visionalgorithms may be executed on CPUs, which may be configured usinghigh-level programming language, such as the C programming language, toexecute a wide variety of processing algorithms across a wide variety ofvisual data. However, CPUs are oftentimes unable to meet the performancerequirements of many computer vision applications, such as those relatedto execution time and power consumption, for example. In particular,many CPUs are unable to execute complex object detection algorithms inreal-time, which is a requirement of in-vehicle ADAS applications, and arequirement for practical Level 3-5 autonomous vehicles.

In contrast to conventional systems, by providing a CPU complex, GPUcomplex, and a hardware acceleration cluster, the technology describedherein allows for multiple neural networks to be performedsimultaneously and/or sequentially, and for the results to be combinedtogether to enable Level 3-5 autonomous driving functionality. Forexample, a CNN executing on the DLA or dGPU (e.g., the GPU(s) 620) mayinclude a text and word recognition, allowing the supercomputer to readand understand traffic signs, including signs for which the neuralnetwork has not been specifically trained. The DLA may further include aneural network that is able to identify, interpret, and providessemantic understanding of the sign, and to pass that semanticunderstanding to the path planning modules running on the CPU Complex.

As another example, multiple neural networks may be run simultaneously,as is required for Level 3, 4, or 5 driving. For example, a warning signconsisting of “Caution: flashing lights indicate icy conditions,” alongwith an electric light, may be independently or collectively interpretedby several neural networks. The sign itself may be identified as atraffic sign by a first deployed neural network (e.g., a neural networkthat has been trained), the text “Flashing lights indicate icyconditions” may be interpreted by a second deployed neural network,which informs the vehicle's path planning software (preferably executingon the CPU Complex) that when flashing lights are detected, icyconditions exist. The flashing light may be identified by operating athird deployed neural network over multiple frames, informing thevehicle's path-planning software of the presence (or absence) offlashing lights. All three neural networks may run simultaneously, suchas within the DLA and/or on the GPU(s) 608.

In some examples, a CNN for facial recognition and vehicle owneridentification may use data from camera sensors to identify the presenceof an authorized driver and/or owner of the vehicle 104. The always onsensor processing engine may be used to unlock the vehicle when theowner approaches the driver door and turn on the lights, and, insecurity mode, to disable the vehicle when the owner leaves the vehicle.In this way, the SoC(s) 604 provide for security against theft and/orcarjacking.

In another example, a CNN for emergency vehicle detection andidentification may use data from microphones 696 to detect and identifyemergency vehicle sirens. In contrast to conventional systems, that usegeneral classifiers to detect sirens and manually extract features, theSoC(s) 604 use the CNN for classifying environmental and urban sounds,as well as classifying visual data. In a preferred embodiment, the CNNrunning on the DLA is trained to identify the relative closing speed ofthe emergency vehicle (e.g., by using the Doppler effect). The CNN mayalso be trained to identify emergency vehicles specific to the localarea in which the vehicle is operating, as identified by GNSS sensor(s)658. Thus, for example, when operating in Europe the CNN will seek todetect European sirens, and when in the United States the CNN will seekto identify only North American sirens. Once an emergency vehicle isdetected, a control program may be used to execute an emergency vehiclesafety routine, slowing the vehicle, pulling over to the side of theroad, parking the vehicle, and/or idling the vehicle, with theassistance of ultrasonic sensors 662, until the emergency vehicle(s)passes.

The vehicle may include a CPU(s) 618 (e.g., discrete CPU(s), ordCPU(s)), that may be coupled to the SoC(s) 604 via a high-speedinterconnect (e.g., PCIe). The CPU(s) 618 may include an X86 processor,for example. The CPU(s) 618 may be used to perform any of a variety offunctions, including arbitrating potentially inconsistent resultsbetween ADAS sensors and the SoC(s) 604, and/or monitoring the statusand health of the controller(s) 636 and/or infotainment SoC 630, forexample.

The vehicle 104 may include a GPU(s) 620 (e.g., discrete GPU(s), ordGPU(s)), that may be coupled to the SoC(s) 604 via a high-speedinterconnect (e.g., NVIDIA's NVLINK). The GPU(s) 620 may provideadditional artificial intelligence functionality, such as by executingredundant and/or different neural networks, and may be used to trainand/or update neural networks based on input (e.g., sensor data) fromsensors of the vehicle 104.

The vehicle 104 may further include the network interface 624 which mayinclude one or more wireless antennas 626 (e.g., one or more wirelessantennas for different communication protocols, such as a cellularantenna, a Bluetooth antenna, etc.). The network interface 624 may beused to enable wireless connectivity over the Internet with the cloud(e.g., with the server(s) 678 and/or other network devices), with othervehicles, and/or with computing devices (e.g., client devices ofpassengers). To communicate with other vehicles, a direct link may beestablished between the two vehicles and/or an indirect link may beestablished (e.g., across networks and over the Internet). Direct linksmay be provided using a vehicle-to-vehicle communication link. Thevehicle-to-vehicle communication link may provide the vehicle 104information about vehicles in proximity to the vehicle 104 (e.g.,vehicles in front of, on the side of, and/or behind the vehicle 104).This functionality may be part of a cooperative adaptive cruise controlfunctionality of the vehicle 104.

The network interface 624 may include a SoC that provides modulation anddemodulation functionality and enables the controller(s) 636 tocommunicate over wireless networks. The network interface 624 mayinclude a radio frequency front-end for up-conversion from baseband toradio frequency, and down conversion from radio frequency to baseband.The frequency conversions may be performed through well-known processes,and/or may be performed using super-heterodyne processes. In someexamples, the radio frequency front end functionality may be provided bya separate chip. The network interface may include wirelessfunctionality for communicating over LTE, WCDMA, UMTS, GSM, CDMA2000,Bluetooth, Bluetooth LE, Wi-Fi, Z-Wave, ZigBee, LoRaWAN, and/or otherwireless protocols.

The vehicle 104 may further include data store(s) 628 which may includeoff-chip (e.g., off the SoC(s) 604) storage. The data store(s) 628 mayinclude one or more storage elements including RAM, SRAM, DRAM, VRAM,Flash, hard disks, and/or other components and/or devices that may storeat least one bit of data.

The vehicle 104 may further include GNSS sensor(s) 658. The GNSSsensor(s) 658 (e.g., GPS and/or assisted GPS sensors), to assist inmapping, perception, occupancy grid generation, and/or path planningfunctions. Any number of GNSS sensor(s) 658 may be used, including, forexample and without limitation, a GPS using a USB connector with anEthernet to Serial (RS-232) bridge.

The vehicle 104 may further include RADAR sensor(s) 660. The RADARsensor(s) 660 may be used by the vehicle 104 for long-range vehicledetection, even in darkness and/or severe weather conditions. RADARfunctional safety levels may be ASIL B. The RADAR sensor(s) 660 may usethe CAN and/or the bus 602 (e.g., to transmit data generated by theRADAR sensor(s) 660) for control and to access object tracking data,with access to Ethernet to access raw data in some examples. A widevariety of RADAR sensor types may be used. For example, and withoutlimitation, the RADAR sensor(s) 660 may be suitable for front, rear, andside RADAR use. In some example, Pulse Doppler RADAR sensor(s) are used.

The RADAR sensor(s) 660 may include different configurations, such aslong range with narrow field of view, short range with wide field ofview, short range side coverage, etc. In some examples, long-range RADARmay be used for adaptive cruise control functionality. The long-rangeRADAR systems may provide a broad field of view realized by two or moreindependent scans, such as within a 250 m range. The RADAR sensor(s) 660may help in distinguishing between static and moving objects, and may beused by ADAS systems for emergency brake assist and forward collisionwarning. Long-range RADAR sensors may include monostatic multimodalRADAR with multiple (e.g., six or more) fixed RADAR antennae and ahigh-speed CAN and FlexRay interface. In an example with six antennae,the central four antennae may create a focused beam pattern, designed torecord the vehicle's 104 surroundings at higher speeds with minimalinterference from traffic in adjacent lanes. The other two antennae mayexpand the field of view, making it possible to quickly detect vehiclesentering or leaving the vehicle's 104 lane.

Mid-range RADAR systems may include, as an example, a range of up to 660m (front) or 80 m (rear), and a field of view of up to 42 degrees(front) or 650 degrees (rear). Short-range RADAR systems may include,without limitation, RADAR sensors designed to be installed at both endsof the rear bumper. When installed at both ends of the rear bumper, sucha RADAR sensor systems may create two beams that constantly monitor theblind spot in the rear and next to the vehicle.

Short-range RADAR systems may be used in an ADAS system for blind spotdetection and/or lane change assist.

The vehicle 104 may further include ultrasonic sensor(s) 662. Theultrasonic sensor(s) 662, which may be positioned at the front, back,and/or the sides of the vehicle 104, may be used for park assist and/orto create and update an occupancy grid. A wide variety of ultrasonicsensor(s) 662 may be used, and different ultrasonic sensor(s) 662 may beused for different ranges of detection (e.g., 2.5 m, 4 m). Theultrasonic sensor(s) 662 may operate at functional safety levels of ASILB.

The vehicle 104 may include LIDAR sensor(s) 664. The LIDAR sensor(s) 664may be used for object and pedestrian detection, emergency braking,collision avoidance, and/or other functions. The LIDAR sensor(s) 664 maybe functional safety level ASIL B. In some examples, the vehicle 104 mayinclude multiple LIDAR sensors 664 (e.g., two, four, six, etc.) that mayuse Ethernet (e.g., to provide data to a Gigabit Ethernet switch).

In some examples, the LIDAR sensor(s) 664 may be capable of providing alist of objects and their distances for a 360-degree field of view.Commercially available LIDAR sensor(s) 664 may have an advertised rangeof approximately 104 m, with an accuracy of 2 cm-3 cm, and with supportfor a 104 Mbps Ethernet connection, for example. In some examples, oneor more non-protruding LIDAR sensors 664 may be used. In such examples,the LIDAR sensor(s) 664 may be implemented as a small device that may beembedded into the front, rear, sides, and/or corners of the vehicle 104.The LIDAR sensor(s) 664, in such examples, may provide up to a620-degree horizontal and 35-degree vertical field-of-view, with a 200 mrange even for low-reflectivity objects. Front-mounted LIDAR sensor(s)664 may be configured for a horizontal field of view between 45 degreesand 135 degrees.

In some examples, LIDAR technologies, such as 3D flash LIDAR, may alsobe used. 3D Flash LIDAR uses a flash of a laser as a transmissionsource, to illuminate vehicle surroundings up to approximately 200 m. Aflash LIDAR unit includes a receptor, which records the laser pulsetransit time and the reflected light on each pixel, which in turncorresponds to the range from the vehicle to the objects. Flash LIDARmay allow for highly accurate and distortion-free images of thesurroundings to be generated with every laser flash. In some examples,four flash LIDAR sensors may be deployed, one at each side of thevehicle 104. Available 3D flash LIDAR systems include a solid-state 3Dstaring array LIDAR camera with no moving parts other than a fan (e.g.,a non-scanning LIDAR device). The flash LIDAR device may use a 5nanosecond class I (eye-safe) laser pulse per frame and may capture thereflected laser light in the form of 3D range point clouds andco-registered intensity data. By using flash LIDAR, and because flashLIDAR is a solid-state device with no moving parts, the LIDAR sensor(s)664 may be less susceptible to motion blur, vibration, and/or shock.

The vehicle may further include IMU sensor(s) 666. The IMU sensor(s) 666may be located at a center of the rear axle of the vehicle 104, in someexamples. The IMU sensor(s) 666 may include, for example and withoutlimitation, an accelerometer(s), a magnetometer(s), a gyroscope(s), amagnetic compass(es), and/or other sensor types. In some examples, suchas in six-axis applications, the IMU sensor(s) 666 may includeaccelerometers and gyroscopes, while in nine-axis applications, the IMUsensor(s) 666 may include accelerometers, gyroscopes, and magnetometers.

In some embodiments, the IMU sensor(s) 666 may be implemented as aminiature, high performance GPS-Aided Inertial Navigation System(GPS/INS) that combines micro-electro-mechanical systems (MEMS) inertialsensors, a high-sensitivity GPS receiver, and advanced Kalman filteringalgorithms to provide estimates of position, velocity, and attitude. Assuch, in some examples, the IMU sensor(s) 666 may enable the vehicle 104to estimate heading without requiring input from a magnetic sensor bydirectly observing and correlating the changes in velocity from GPS tothe IMU sensor(s) 666. In some examples, the IMU sensor(s) 666 and theGNSS sensor(s) 658 may be combined in a single integrated unit.

The vehicle may include microphone(s) 696 placed in and/or around thevehicle 104. The microphone(s) 696 may be used for emergency vehicledetection and identification, among other things.

The vehicle may further include any number of camera types, includingstereo camera(s) 668, wide-view camera(s) 670, infrared camera(s) 672,surround camera(s) 674, long-range and/or mid-range camera(s) 698,and/or other camera types. The cameras may be used to capture image dataaround an entire periphery of the vehicle 104. The types of cameras useddepends on the embodiments and requirements for the vehicle 104, and anycombination of camera types may be used to provide the necessarycoverage around the vehicle 104. In addition, the number of cameras maydiffer depending on the embodiment. For example, the vehicle may includesix cameras, seven cameras, ten cameras, twelve cameras, and/or anothernumber of cameras. The cameras may support, as an example and withoutlimitation, Gigabit Multimedia Serial Link (GMSL) and/or GigabitEthernet. Each of the camera(s) is described with more detail hereinwith respect to FIG. 6A and FIG. 6B.

The vehicle 104 may further include vibration sensor(s) 642. Thevibration sensor(s) 642 may measure vibrations of components of thevehicle, such as the axle(s). For example, changes in vibrations mayindicate a change in road surfaces. In another example, when two or morevibration sensors 642 are used, the differences between the vibrationsmay be used to determine friction or slippage of the road surface (e.g.,when the difference in vibration is between a power-driven axle and afreely rotating axle).

The vehicle 104 may include an ADAS system 638. The ADAS system 638 mayinclude a SoC, in some examples. The ADAS system 638 may includeautonomous/adaptive/automatic cruise control (ACC), cooperative adaptivecruise control (CACC), forward crash warning (FCW), automatic emergencybraking (AEB), lane departure warnings (LDW), lane keep assist (LKA),blind spot warning (BSW), rear cross-traffic warning (RCTW), collisionwarning systems (CWS), lane centering (LC), and/or other features andfunctionality.

The ACC systems may use RADAR sensor(s) 660, LIDAR sensor(s) 664, and/ora camera(s). The ACC systems may include longitudinal ACC and/or lateralACC. Longitudinal ACC monitors and controls the distance to the vehicleimmediately ahead of the vehicle 104 and automatically adjust thevehicle speed to maintain a safe distance from vehicles ahead. LateralACC performs distance keeping, and advises the vehicle 104 to changelanes when necessary. Lateral ACC is related to other ADAS applicationssuch as LCA and CWS.

CACC uses information from other vehicles that may be received via thenetwork interface 624 and/or the wireless antenna(s) 626 from othervehicles via a wireless link, or indirectly, over a network connection(e.g., over the Internet). Direct links may be provided by avehicle-to-vehicle (V2V) communication link, while indirect links may beinfrastructure-to-vehicle (I2V) communication link. In general, the V2Vcommunication concept provides information about the immediatelypreceding vehicles (e.g., vehicles immediately ahead of and in the samelane as the vehicle 104), while the I2V communication concept providesinformation about traffic further ahead. CACC systems may include eitheror both I2V and V2V information sources. Given the information of thevehicles ahead of the vehicle 104, CACC may be more reliable and it haspotential to improve traffic flow smoothness and reduce congestion onthe road.

FCW systems are designed to alert the driver to a hazard, so that thedriver may take corrective action. FCW systems use a front-facing cameraand/or RADAR sensor(s) 660, coupled to a dedicated processor, DSP, FPGA,and/or ASIC, that is electrically coupled to driver feedback, such as adisplay, speaker, and/or vibrating component. FCW systems may provide awarning, such as in the form of a sound, visual warning, vibrationand/or a quick brake pulse.

AEB systems detect an impending forward collision with another vehicleor other object, and may automatically apply the brakes if the driverdoes not take corrective action within a specified time or distanceparameter. AEB systems may use front-facing camera(s) and/or RADARsensor(s) 660, coupled to a dedicated processor, DSP, FPGA, and/or ASIC.When the AEB system detects a hazard, it typically first alerts thedriver to take corrective action to avoid the collision and, if thedriver does not take corrective action, the AEB system may automaticallyapply the brakes in an effort to prevent, or at least mitigate, theimpact of the predicted collision. AEB systems, may include techniquessuch as dynamic brake support and/or crash imminent braking.

LDW systems provide visual, audible, and/or tactile warnings, such assteering wheel or seat vibrations, to alert the driver when the vehicle104 crosses lane markings. A LDW system does not activate when thedriver indicates an intentional lane departure, by activating a turnsignal. LDW systems may use front-side facing cameras, coupled to adedicated processor, DSP, FPGA, and/or ASIC, that is electricallycoupled to driver feedback, such as a display, speaker, and/or vibratingcomponent.

LKA systems are a variation of LDW systems. LKA systems provide steeringinput or braking to correct the vehicle 104 if the vehicle 104 starts toexit the lane.

BSW systems detects and warn the driver of vehicles in an automobile'sblind spot. BSW systems may provide a visual, audible, and/or tactilealert to indicate that merging or changing lanes is unsafe. The systemmay provide an additional warning when the driver uses a turn signal.BSW systems may use rear-side facing camera(s) and/or RADAR sensor(s)660, coupled to a dedicated processor, DSP, FPGA, and/or ASIC, that iselectrically coupled to driver feedback, such as a display, speaker,and/or vibrating component.

RCTW systems may provide visual, audible, and/or tactile notificationwhen an object is detected outside the rear-camera range when thevehicle 104 is backing up. Some RCTW systems include AEB to ensure thatthe vehicle brakes are applied to avoid a crash. RCTW systems may useone or more rear-facing RADAR sensor(s) 660, coupled to a dedicatedprocessor, DSP, FPGA, and/or ASIC, that is electrically coupled todriver feedback, such as a display, speaker, and/or vibrating component.

Conventional ADAS systems may be prone to false positive results whichmay be annoying and distracting to a driver, but typically are notcatastrophic, because the ADAS systems alert the driver and allow thedriver to decide whether a safety condition truly exists and actaccordingly. However, in an autonomous vehicle 104, the vehicle 104itself must, in the case of conflicting results, decide whether to heedthe result from a primary computer or a secondary computer (e.g., afirst controller 636 or a second controller 636). For example, in someembodiments, the ADAS system 638 may be a backup and/or secondarycomputer for providing perception information to a backup computerrationality module. The backup computer rationality monitor may run aredundant diverse software on hardware components to detect faults inperception and dynamic driving tasks. Outputs from the ADAS system 638may be provided to a supervisory MCU. If outputs from the primarycomputer and the secondary computer conflict, the supervisory MCU mustdetermine how to reconcile the conflict to ensure safe operation.

In some examples, the primary computer may be configured to provide thesupervisory MCU with a confidence score, indicating the primarycomputer's confidence in the chosen result. If the confidence scoreexceeds a threshold, the supervisory MCU may follow the primarycomputer's direction, regardless of whether the secondary computerprovides a conflicting or inconsistent result. Where the confidencescore does not meet the threshold, and where the primary and secondarycomputer indicate different results (e.g., the conflict), thesupervisory MCU may arbitrate between the computers to determine theappropriate outcome.

The supervisory MCU may be configured to run a neural network(s) that istrained and configured to determine, based on outputs from the primarycomputer and the secondary computer, conditions under which thesecondary computer provides false alarms. Thus, the neural network(s) inthe supervisory MCU may learn when the secondary computer's output maybe trusted, and when it cannot. For example, when the secondary computeris a RADAR-based FCW system, a neural network(s) in the supervisory MCUmay learn when the FCW system is identifying metallic objects that arenot, in fact, hazards, such as a drainage grate or manhole cover thattriggers an alarm. Similarly, when the secondary computer is acamera-based LDW system, a neural network in the supervisory MCU maylearn to override the LDW when bicyclists or pedestrians are present anda lane departure is, in fact, the safest maneuver. In embodiments thatinclude a neural network(s) running on the supervisory MCU, thesupervisory MCU may include at least one of a DLA or GPU suitable forrunning the neural network(s) with associated memory. In preferredembodiments, the supervisory MCU may comprise and/or be included as acomponent of the SoC(s) 604.

In other examples, ADAS system 638 may include a secondary computer thatperforms ADAS functionality using traditional rules of computer vision.As such, the secondary computer may use classic computer vision rules(if-then), and the presence of a neural network(s) in the supervisoryMCU may improve reliability, safety and performance. For example, thediverse implementation and intentional non-identity makes the overallsystem more fault-tolerant, especially to faults caused by software (orsoftware-hardware interface) functionality. For example, if there is asoftware bug or error in the software running on the primary computer,and the non-identical software code running on the secondary computerprovides the same overall result, the supervisory MCU may have greaterconfidence that the overall result is correct, and the bug in softwareor hardware on primary computer is not causing material error.

In some examples, the output of the ADAS system 638 may be fed into theprimary computer's perception block and/or the primary computer'sdynamic driving task block. For example, if the ADAS system 638indicates a forward crash warning due to an object immediately ahead,the perception block may use this information when identifying objects.In other examples, the secondary computer may have its own neuralnetwork which is trained and thus reduces the risk of false positives,as described herein.

The vehicle 104 may further include the infotainment SoC 630 (e.g., anin-vehicle infotainment system (IVI)). Although illustrated anddescribed as a SoC, the infotainment system may not be a SoC, and mayinclude two or more discrete components. The infotainment SoC 630 mayinclude a combination of hardware and software that may be used toprovide audio (e.g., music, a personal digital assistant, navigationalinstructions, news, radio, etc.), video (e.g., TV, movies, streaming,etc.), phone (e.g., hands-free calling), network connectivity (e.g.,LTE, Wi-Fi, etc.), and/or information services (e.g., navigationsystems, rear-parking assistance, a radio data system, vehicle relatedinformation such as fuel level, total distance covered, brake fuellevel, oil level, door open/close, air filter information, etc.) to thevehicle 104. For example, the infotainment SoC 630 may radios, diskplayers, navigation systems, video players, USB and Bluetoothconnectivity, carputers, in-car entertainment, Wi-Fi, steering wheelaudio controls, hands free voice control, a heads-up display (HUD), anHMI display 634, a telematics device, a control panel (e.g., forcontrolling and/or interacting with various components, features, and/orsystems), and/or other components. The infotainment SoC 630 may furtherbe used to provide information (e.g., visual and/or audible) to auser(s) of the vehicle, such as information from the ADAS system 638,autonomous driving information such as planned vehicle maneuvers,trajectories, surrounding environment information (e.g., intersectioninformation, vehicle information, road information, etc.), and/or otherinformation.

The infotainment SoC 630 may include GPU functionality. The infotainmentSoC 630 may communicate over the bus 602 (e.g., CAN bus, Ethernet, etc.)with other devices, systems, and/or components of the vehicle 104. Insome examples, the infotainment SoC 630 may be coupled to a supervisoryMCU such that the GPU of the infotainment system may perform someself-driving functions in the event that the primary controller(s) 636(e.g., the primary and/or backup computers of the vehicle 104) fail. Insuch an example, the infotainment SoC 630 may put the vehicle 104 into achauffeur to safe stop mode, as described herein.

The vehicle 104 may further include an instrument cluster 632 (e.g., adigital dash, an electronic instrument cluster, a digital instrumentpanel, etc.). The instrument cluster 632 may include a controller and/orsupercomputer (e.g., a discrete controller or supercomputer). Theinstrument cluster 632 may include a set of instrumentation such as aspeedometer, fuel level, oil pressure, tachometer, odometer, turnindicators, gearshift position indicator, seat belt warning light(s),parking-brake warning light(s), engine-malfunction light(s), airbag(SRS) system information, lighting controls, safety system controls,navigation information, etc. In some examples, information may bedisplayed and/or shared among the infotainment SoC 630 and theinstrument cluster 632. In other words, the instrument cluster 632 maybe included as part of the infotainment SoC 630, or vice versa.

FIG. 6D is a system diagram for communication between cloud-basedserver(s) and the example autonomous vehicle 104 of FIG. 6A, inaccordance with some embodiments of the present disclosure. The system676 may include server(s) 678, network(s) 690, and vehicles, includingthe vehicle 104. The server(s) 678 may include a plurality of GPUs684(A)-684(H) (collectively referred to herein as GPUs 684), PCIeswitches 682(A)-682(H) (collectively referred to herein as PCIe switches682), and/or CPUs 680(A)-680(B) (collectively referred to herein as CPUs680). The GPUs 684, the CPUs 680, and the PCIe switches may beinterconnected with high-speed interconnects such as, for example andwithout limitation, NVLink interfaces 688 developed by NVIDIA and/orPCIe connections 686. In some examples, the GPUs 684 are connected viaNVLink and/or NVSwitch SoC and the GPUs 684 and the PCIe switches 682are connected via PCIe interconnects. Although eight GPUs 684, two CPUs680, and two PCIe switches are illustrated, this is not intended to belimiting. Depending on the embodiment, each of the server(s) 678 mayinclude any number of GPUs 684, CPUs 680, and/or PCIe switches. Forexample, the server(s) 678 may each include eight, sixteen, thirty-two,and/or more GPUs 684.

The server(s) 678 may receive, over the network(s) 690 and from thevehicles, image data representative of images showing unexpected orchanged road conditions, such as recently commenced road-work. Theserver(s) 678 may transmit, over the network(s) 690 and to the vehicles,neural networks 692, updated neural networks 692, and/or map information694, including information regarding traffic and road conditions. Theupdates to the map information 694 may include updates for the HD map622, such as information regarding construction sites, potholes,detours, flooding, and/or other obstructions. In some examples, theneural networks 692, the updated neural networks 692, and/or the mapinformation 694 may have resulted from new training and/or experiencesrepresented in data received from any number of vehicles in theenvironment, and/or based on training performed at a datacenter (e.g.,using the server(s) 678 and/or other servers).

The server(s) 678 may be used to train machine learning models (e.g.,neural networks) based on training data. The training data may begenerated by the vehicles, and/or may be generated in a simulation(e.g., using a game engine). In some examples, the training data istagged (e.g., where the neural network benefits from supervisedlearning) and/or undergoes other pre-processing, while in other examplesthe training data is not tagged and/or pre-processed (e.g., where theneural network does not require supervised learning). Once the machinelearning models are trained, the machine learning models may be used bythe vehicles (e.g., transmitted to the vehicles over the network(s) 690,and/or the machine learning models may be used by the server(s) 678 toremotely monitor the vehicles.

In some examples, the server(s) 678 may receive data from the vehiclesand apply the data to up-to-date real-time neural networks for real-timeintelligent inferencing. The server(s) 678 may include deep-learningsupercomputers and/or dedicated AI computers powered by GPU(s) 684, suchas a DGX and DGX Station machines developed by NVIDIA. However, in someexamples, the server(s) 678 may include deep learning infrastructurethat use only CPU-powered datacenters.

The deep-learning infrastructure of the server(s) 678 may be capable offast, real-time inferencing, and may use that capability to evaluate andverify the health of the processors, software, and/or associatedhardware in the vehicle 104. For example, the deep-learninginfrastructure may receive periodic updates from the vehicle 104, suchas a sequence of images and/or objects that the vehicle 104 has locatedin that sequence of images (e.g., via computer vision and/or othermachine learning object classification techniques). The deep-learninginfrastructure may run its own neural network to identify the objectsand compare them with the objects identified by the vehicle 104 and, ifthe results do not match and the infrastructure concludes that the AI inthe vehicle 104 is malfunctioning, the server(s) 678 may transmit asignal to the vehicle 104 instructing a fail-safe computer of thevehicle 104 to assume control, notify the passengers, and complete asafe parking maneuver.

For inferencing, the server(s) 678 may include the GPU(s) 684 and one ormore programmable inference accelerators (e.g., NVIDIA's TensorRT 3).The combination of GPU-powered servers and inference acceleration maymake real-time responsiveness possible. In other examples, such as whereperformance is less critical, servers powered by CPUs, FPGAs, and otherprocessors may be used for inferencing.

Generalized Computing Device

FIG. 7 is a block diagram of an example of the computing device 700suitable for use in implementing some embodiments of the presentdisclosure. The computing device 700 may include a bus 702 that directlyor indirectly couples the following devices: memory 704, one or morecentral processing units (CPUs) 706, one or more graphics processingunits (GPUs) 708, a communication interface 710, input/output (I/O)ports 712, input/output components 714, a power supply 716, and one ormore presentation components 718 (e.g., display(s)). In addition to CPU706 and GPU 708, computing device 700 may include additional logicdevices that are not shown in FIG. 7 , such as but not limited to animage signal processor (ISP), a digital signal processor (DSP), an ASIC,an FPGA, or the like.

Although the various blocks of FIG. 7 are shown as connected via the bus702 with lines, this is not intended to be limiting and is for clarityonly. For example, in some embodiments, a presentation component 718,such as a display device, may be considered an I/O component 714 (e.g.,if the display is a touch screen). As another example, the CPUs 706and/or GPUs 708 may include memory (e.g., the memory 704 may berepresentative of a storage device in addition to the memory of the GPUs708, the CPUs 706, and/or other components). In other words, thecomputing device of FIG. 7 is merely illustrative. Distinction is notmade between such categories as “workstation,” “server,” “laptop,”“desktop,” “tablet,” “client device,” “mobile device,” “handhelddevice,” “game console,” “electronic control unit (ECU),” “virtualreality system,” and/or other device or system types, as all arecontemplated within the scope of the computing device of FIG. 7 .

The bus 702 may represent one or more busses, such as an address bus, adata bus, a control bus, or a combination thereof. The bus 702 mayinclude one or more bus types, such as an industry standard architecture(ISA) bus, an extended industry standard architecture (EISA) bus, avideo electronics standards association (VESA) bus, a peripheralcomponent interconnect (PCI) bus, a peripheral component interconnectexpress (PCIe) bus, and/or another type of bus.

The memory 704 may include any of a variety of computer-readable media.The computer-readable media may be any available media that can beaccessed by the computing device 700. The computer-readable media mayinclude both volatile and nonvolatile media, and removable andnon-removable media. By way of example, and not limitation, thecomputer-readable media may comprise computer-storage media andcommunication media.

The computer-storage media may include both volatile and nonvolatilemedia and/or removable and non-removable media implemented in any methodor technology for storage of information such as computer-readableinstructions, data structures, program modules, and/or other data types.For example, the memory 704 may store computer-readable instructions(e.g., that represent a program(s) and/or a program element(s), such asan operating system. Computer-storage media may include, but is notlimited to, RAM, ROM, EEPROM, flash memory or other memory technology,CD-ROM, digital versatile disks (DVD) or other optical disk storage,magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, or any other medium which can be used to storethe desired information and which can be accessed by the computingdevice 700. As used herein, computer storage media does not comprisesignals per se.

The communication media may embody computer-readable instructions, datastructures, program modules, and/or other data types in a modulated datasignal such as a carrier wave or other transport mechanism and includesany information delivery media. The term “modulated data signal” mayrefer to a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in the signal. By wayof example, and not limitation, the communication media may includewired media such as a wired network or direct-wired connection, andwireless media such as acoustic, RF, infrared and other wireless media.Combinations of any of the above should also be included within thescope of computer-readable media.

The CPU(s) 706 may be configured to execute the computer-readableinstructions to control one or more components of the computing device700 to perform one or more of the methods and/or processes describedherein. The CPU(s) 706 may each include one or more cores (e.g., one,two, four, eight, twenty-eight, seventy-two, etc.) that are capable ofhandling a multitude of software threads simultaneously. The CPU(s) 706may include any type of processor, and may include different types ofprocessors depending on the type of the computing device 700 implemented(e.g., processors with fewer cores for mobile devices and processorswith more cores for servers). For example, depending on the type of thecomputing device 700, the processor may be an ARM processor implementedusing Reduced Instruction Set Computing (RISC) or an x86 processorimplemented using Complex Instruction Set Computing (CISC). Thecomputing device 700 may include one or more CPUs 706 in addition to oneor more microprocessors or supplementary co-processors, such as mathco-processors.

The GPU(s) 708 may be used by the computing device 700 to rendergraphics (e.g., 3D graphics). The GPU(s) 708 may include hundreds orthousands of cores that are capable of handling hundreds or thousands ofsoftware threads simultaneously. The GPU(s) 708 may generate pixel datafor output images in response to rendering commands (e.g., renderingcommands from the CPU(s) 706 received via a host interface). The GPU(s)708 may include graphics memory, such as display memory, for storingpixel data. The display memory may be included as part of the memory704. The GPU(s) 708 may include two or more GPUs operating in parallel(e.g., via a link). When combined together, each GPU 708 can generatepixel data for different portions of an output image or for differentoutput images (e.g., a first GPU for a first image and a second GPU fora second image). Each GPU can include its own memory, or can sharememory with other GPUs.

In examples where the computing device 700 does not include the GPU(s)708, the CPU(s) 706 may be used to render graphics.

The communication interface 710 may include one or more receivers,transmitters, and/or transceivers that enable the computing device 700to communicate with other computing devices via an electroniccommunication network, included wired and/or wireless communications.The communication interface 710 may include components and functionalityto enable communication over any of a number of different networks, suchas wireless networks (e.g., Wi-Fi, Z-Wave, Bluetooth, Bluetooth LE,ZigBee, etc.), wired networks (e.g., communicating over Ethernet),low-power wide-area networks (e.g., LoRaWAN, SigFox, etc.), and/or theInternet.

The I/O ports 712 may enable the computing device 700 to be logicallycoupled to other devices including the I/O components 714, thepresentation component(s) 718, and/or other components, some of whichmay be built in to (e.g., integrated in) the computing device 700.Illustrative I/O components 714 include a microphone, mouse, keyboard,joystick, game pad, game controller, satellite dish, scanner, printer,wireless device, etc. The I/O components 714 may provide a natural userinterface (NUI) that processes air gestures, voice, or otherphysiological inputs generated by a user. In some instances, inputs maybe transmitted to an appropriate network element for further processing.An NUI may implement any combination of speech recognition, stylusrecognition, facial recognition, biometric recognition, gesturerecognition both on screen and adjacent to the screen, air gestures,head and eye tracking, and touch recognition (as described in moredetail below) associated with a display of the computing device 700. Thecomputing device 700 may be include depth cameras, such as stereoscopiccamera systems, infrared camera systems, RGB camera systems, touchscreentechnology, and combinations of these, for gesture detection andrecognition. Additionally, the computing device 700 may includeaccelerometers or gyroscopes (e.g., as part of an inertia measurementunit (IMU)) that enable detection of motion. In some examples, theoutput of the accelerometers or gyroscopes may be used by the computingdevice 700 to render immersive augmented reality or virtual reality.

The power supply 716 may include a hard-wired power supply, a batterypower supply, or a combination thereof. The power supply 716 may providepower to the computing device 700 to enable the components of thecomputing device 700 to operate.

The presentation component(s) 718 may include a display (e.g., amonitor, a touch screen, a television screen, a heads-up-display (HUD),other display types, or a combination thereof), speakers, and/or otherpresentation components. The presentation component(s) 718 may receivedata from other components (e.g., the GPU(s) 708, the CPU(s) 706, etc.),and output the data (e.g., as an image, video, sound, etc.).

The disclosure may be described in the general context of computer codeor machine-useable instructions, including computer-executableinstructions such as program modules, being executed by a computer orother machine, such as a personal data assistant or other handhelddevice. Generally, program modules including routines, programs,objects, components, data structures, etc., refer to code that performparticular tasks or implement particular abstract data types. Thedisclosure may be practiced in a variety of system configurations,including handheld devices, consumer electronics, general-purposecomputers, more specialty computing devices, etc. The disclosure mayalso be practiced in distributed computing environments where tasks areperformed by remote-processing devices that are linked through acommunications network.

As used herein, a recitation of “and/or” with respect to two or moreelements should be interpreted to mean only one element, or acombination of elements. For example, “element A, element B, and/orelement C” may include only element A, only element B, only element C,element A and element B, element A and element C, element B and elementC, or elements A, B, and C. In addition, “at least one of element A orelement B” may include at least one of element A, at least one ofelement B, or at least one of element A and at least one of element B.

The subject matter of the present disclosure is described withspecificity herein to meet statutory requirements. However, thedescription itself is not intended to limit the scope of thisdisclosure. Rather, the inventors have contemplated that the claimedsubject matter might also be embodied in other ways, to includedifferent steps or combinations of steps similar to the ones describedin this document, in conjunction with other present or futuretechnologies. Moreover, although the terms “step” and/or “block” may beused herein to connote different elements of methods employed, the termsshould not be interpreted as implying any particular order among orbetween various steps herein disclosed unless and except when the orderof individual steps is explicitly described.

What is claimed is:
 1. A method comprising: determining tone controlpoints based at least on first pixel values defined by first image data;computing a rate of change for a tone mapping function based at least ona first tone control point of the tone control points; fitting a curveof the tone mapping function to a second tone control point of the tonecontrol points based at least on constraining the tone mapping functionto the rate of change at the second tone control point; and generatingsecond image data defining second pixel values using the tone mappingfunction.
 2. The method of claim 1, wherein the rate of changecorresponds to the first tone control point and a third control point ofthe tone control points, and the curve is fit to the second tone controlpoint and the third control point using the rate of change.
 3. Themethod of claim 1, wherein the first image data comprises high dynamicrange (HDR) image data and the generating of the second image dataincludes converting the HDR image data to lower dynamic range image datausing the tone mapping function.
 4. The method of claim 1, wherein thetone mapping function maps a first input value to a second output valuethat is different than a value of the first tone control point.
 5. Themethod of claim 1, wherein the first tone control point is computedbased at least on the second tone control point corresponding to amiddle color value of the first image data.
 6. The method of claim 1,wherein the first tone control point defines a suppression threshold ofthe tone mapping function.
 7. The method of claim 1, wherein the firsttone control point defines a flare-suppression threshold of the tonemapping function.
 8. The method of claim 1, wherein the curve passesthrough a high-tone point of the tone control points that defines ahighlight-compression threshold of the tone mapping function.
 9. Themethod of claim 1, wherein the first image data was captured by at leastone camera device corresponding to a vehicle and the second image datais used as input to execute one or more autonomous control operationscorresponding to the vehicle.
 10. A system comprising: one or moreprocessing units to execute operations comprising: determining tonecontrol points based at least on color information of one or more firstimages; compute a gain value based at least on a first tone controlpoint of the tone control points; fit the tone mapping function to asecond tone control point of the tone control points based at least onconstraining a rate of change of the tone mapping function at the secondtone control point using the gain value; and generate image datarepresentative of one or more second images using the tone mappingfunction.
 11. The system of claim 10, wherein the gain value representsthe rate of change.
 12. The system of claim 10, wherein the one or morefirst images include one or more high dynamic range (HDR) images and thegenerating of the image data includes converting the one or more HDRimages to one or more lower dynamic range images using the tone mappingfunction.
 13. The system of claim 10, wherein the gain value correspondsto a line passing through the first tone control point and a third tonecontrol point of the tone control points.
 14. The system of claim 10,wherein the first tone control point represents a flare-suppressionthreshold of the tone mapping function.
 15. The system of claim 10,wherein the tone mapping function defines a global tone curveconstrained to pass through a plurality of the tone control pointswithout passing through the first tone control point.
 16. The system ofclaim 10, wherein the system is incorporated in an autonomous vehicle,the autonomous vehicle comprising an image sensor that generates atleast one of the one or more first images, wherein at least some of theimage data is used as input to one or more deep neural networksproducing outputs used to control the autonomous vehicle.
 17. Aprocessor comprising: one or more circuits to: determine a gain valuebased at least on a first tone control point of a set of tone controlpoints defined by color information of one or more first images; fit atone mapping function to a second tone control point of the set of tonecontrol points based at least on constraining a slope of the tonemapping function at the second tone control point to correspond to thegain value; and generate one or more second images using the tonemapping function.
 18. The processor of claim 17, wherein the one or morecircuits are to compute the first tone control point based at least on aquantity of mid-tone color values in the one or more first images. 19.The processor of claim 17, wherein the first tone control pointcomprises a suppression threshold point, the second tone control pointcomprises a mid-tone control point, the set of tone control pointsfurther includes a low-tone control point and a high-tone control point,and the slope defines a curve including the low-tone control point, themid-tone control point, and the high-tone control point.
 20. Theprocessor of claim 17, wherein constraining the slope of the tonemapping function includes fitting parameters of a parametric function tothe set of tone control points using the gain value.
 21. The processorof claim 17, wherein the gain value indicates the slope at the secondtone control point.
 22. The processor of claim 17, wherein the gainvalue corresponds to a line passing through the first tone control pointand the second tone control point of the set of tone control points, theline being separate from the tone mapping function.